A Comprehensive Survey of Evolutionary-Based Multiobjective Optimization Techniques

A multi-objective optimisation approach with improved pareto-optimal solutions to enhance economic and environmental dispatch in power systems

This work implements the recently developed nth state Markovian jumping particle swarm optimisation (PSO) algorithm with local search (NS-MJPSOloc) awareness method to address the economic/environmental dispatch (EED) problem. The proposed approach, known as the Non-dominated Sorting Multi-objective PSO with Local Best (NS-MJPSOloc), aims to enhance the performance of the PSO algorithm in multi-objective optimisation problems. This is achieved by redefining the concept of best local candidates within the search space of multi-objective optimisation. The NS-MJPSOloc algorithm uses an evolutionary factor-based mechanism to identify the optimum compromise solution, a Markov chain state jumping technique to control the Pareto-optimal set size, and a neighbourhood's topology (such as a ring or a star) to determine its size. Economic dispatch refers to the systematic allocation of available power resources in order to fulfill all relevant limitations and effectively meet the demand for electricity at the lowest possible operating cost. As a result of heightened public consciousness regarding environmental pollution and the implementation of clean air amendments, nations worldwide have compelled utilities to adapt their operational practises in order to comply with environmental regulations. The (NS-MJPSOloc) approach has been utilised for resolving the EED problem, including cost and emission objectives that are not commensurable. The findings illustrate the efficacy of the suggested (NS-MJPSOloc) approach in producing a collection of Pareto-optimal solutions that are evenly dispersed within a single iteration. The comparison of several approaches reveals the higher performance of the suggested (NS-MJPSOloc) in terms of the diversity of the Pareto-optimal solutions achieved. In addition, a measure of solution quality based on Pareto optimality has been incorporated. The findings validate the effectiveness of the proposed (NS-MJPSOloc) approach in addressing the multi-objective EED issue and generating a trade-off solution that is both optimal and of high quality. We observed that our approach can reduce ∼ 6.4% of fuel costs and ∼ 9.1% of computational time in comparison to the classical PSO technique. Furthermore, our method can reduce ∼ 9.4% of the emissions measured in tons per hour as compared to the PSO approach.

Multi-objective liver cancer algorithm: A novel algorithm for solving engineering design problems

This research introduces the Multi-Objective Liver Cancer Algorithm (MOLCA), a novel approach inspired by the growth and proliferation patterns of liver tumors. MOLCA emulates the evolutionary tendencies of liver tumors, leveraging their expansion dynamics as a model for solving multi-objective optimization problems in engineering design. The algorithm uniquely combines genetic operators with the Random Opposition-Based Learning (ROBL) strategy, optimizing both local and global search capabilities. Further enhancement is achieved through the integration of elitist non-dominated sorting (NDS), information feedback mechanism (IFM) and Crowding Distance (CD) selection method, which collectively aim to efficiently identify the Pareto optimal front. The performance of MOLCA is rigorously assessed using a comprehensive set of standard multi-objective test benchmarks, including ZDT, DTLZ and various Constraint (CONSTR, TNK, SRN, BNH, OSY and KITA) and real-world engineering design problems like Brushless DC wheel motor, Safety isolating transformer, Helical spring, Two-bar truss and Welded beam. Its efficacy is benchmarked against prominent algorithms such as the non-dominated sorting grey wolf optimizer (NSGWO), multiobjective multi-verse optimization (MOMVO), non-dominated sorting genetic algorithm (NSGA-II), decomposition-based multiobjective evolutionary algorithm (MOEA/D) and multiobjective marine predator algorithm (MOMPA). Quantitative analysis is conducted using GD, IGD, SP, SD, HV and RT metrics to represent convergence and distribution, while qualitative aspects are presented through graphical representations of the Pareto fronts. The MOLCA source code is available at: https://github.com/kanak02/MOLCA.

Artificial selection methods from evolutionary computing show promise for directed evolution of microbes

Directed microbial evolution harnesses evolutionary processes in the laboratory to construct microorganisms with enhanced or novel functional traits. Attempting to direct evolutionary processes for applied goals is fundamental to evolutionary computation, which harnesses the principles of Darwinian evolution as a general-purpose search engine for solutions to challenging computational problems. Despite their overlapping approaches, artificial selection methods from evolutionary computing are not commonly applied to living systems in the laboratory. In this work, we ask whether parent selection algorithms-procedures for choosing promising progenitors-from evolutionary computation might be useful for directing the evolution of microbial populations when selecting for multiple functional traits. To do so, we introduce an agent-based model of directed microbial evolution, which we used to evaluate how well three selection algorithms from evolutionary computing (tournament selection, lexicase selection, and non-dominated elite selection) performed relative to methods commonly used in the laboratory (elite and top 10% selection). We found that multiobjective selection techniques from evolutionary computing (lexicase and non-dominated elite) generally outperformed the commonly used directed evolution approaches when selecting for multiple traits of interest. Our results motivate ongoing work transferring these multiobjective selection procedures into the laboratory and a continued evaluation of more sophisticated artificial selection methods.

An Image Encryption Scheme Synchronizing Optimized Chaotic Systems Implemented on Raspberry Pis

Guaranteeing security in information exchange is a challenge in public networks, such as in the highly popular application layer Message Queue Telemetry Transport (MQTT) protocol. On the one hand, chaos generators have shown their usefulness in masking data that can be recovered while having the appropriate binary string. Privacy can then be accomplished by implementing synchronization techniques to connect the transmitter and receiver, among millions of users, to encrypt and decrypt data having the correct public key. On the other hand, chaotic binary sequences can be generated on Rapsberry Pis that can be connected over MQTT. To provide privacy and security, the transmitter and receiver (among millions of devices) can be synchronized to have the same chaotic public key to encrypt and decrypt data. In this manner, this paper shows the implementation of optimized chaos generators on Raspberry Pis that are wirelessly connected via MQTT for the IoT protocol. The publisher encrypts data that are public to millions of interconnected devices, but the data are decrypted by the subscribers having the correct chaotic binary sequence. The image encryption system is tested by performing NIST, TestU01, NPCR, UACI and other statistical analyses.

Last-mile drone delivery combinatorial double auction model using multi-objective evolutionary algorithms

This study proposes a combinatorial double auction bi-objective winner determination problem for last-mile delivery using drone. Prior studies are limited on solving mixed integer model, which are not efficient for large-scale scenario. However, this is not practical in real cases as the computation time to obtain the solution is longer due to number of combinations of packages and participants anticipated in the last-mile delivery platform. Four multi-objective evolutionary algorithms (MOEAs) with the decomposed winner determination problem model are experimented. This study is able to yield Pareto optimal solutions from multiple runs of mixed linear integer programming (MILP) using different objectives weights in the model. Unmanned aerial vehicle, or drone, has potential to reduce cost and save time for last-mile logistic operations. The result positively shows MOEAs are more efficient than MILP in yielding a set of feasible solutions for undertaking complex winner determination problem models. The percentage of improvement in terms of time spent identifying the best option is almost 100%. This is likely an unprecedented research in drone where combinatorial double auction is applied to complex drone delivery services and MOEAs are used to solve the associated winner determination problem model.

Socially Aware Navigation: A Non-linear Multi-objective Optimization Approach

Mobile robots are increasingly populating homes, hospitals, shopping malls, factory floors, and other human environments. Human society has social norms that people mutually accept; obeying these norms is an essential signal that someone is participating socially with respect to the rest of the population. For robots to be socially compatible with humans, it is crucial for robots to obey these social norms. In prior work, we demonstrated a Socially-Aware Navigation (SAN) planner, based on Pareto Concavity Elimination Transformation (PaCcET), in a hallway scenario, optimizing two objectives so the robot does not invade the personal space of people. This article extends our PaCcET-based SAN planner to multiple scenarios with more than two objectives. We modified the Robot Operating System’s (ROS) navigation stack to include PaCcET in the local planning task. We show that our approach can accommodate multiple Human-Robot Interaction (HRI) scenarios. Using the proposed approach, we achieved successful HRI in multiple scenarios such as hallway interactions, an art gallery, waiting in a queue, and interacting with a group. We implemented our method on a simulated PR2 robot in a 2D simulator (Stage) and a pioneer-3DX mobile robot in the real-world to validate all the scenarios. A comprehensive set of experiments shows that our approach can handle multiple interaction scenarios on both holonomic and non-holonomic robots; hence, it can be a viable option for a Unified Socially-Aware Navigation (USAN).

Wind Farm Area Shape Optimization Using Newly Developed Multi-Objective Evolutionary Algorithms

In recent years, wind farm layout optimization (WFLO) has been extendedly developed to address the minimization of turbine wake effects in a wind farm. Considering that increasing the degrees of freedom in the decision space can lead to more efficient solutions in an optimization problem, in this work the WFLO problem that grants total freedom to the wind farm area shape is addressed for the first time. We apply multi-objective optimization with the power output (PO) and the electricity cable length (CL) as objective functions in Horns Rev I (Denmark) via 13 different genetic algorithms: a traditionally used algorithm, a newly developed algorithm, and 11 hybridizations resulted from the two. Turbine wakes and their interactions in the wind farm are computed through the in-house Gaussian wake model. Results show that several of the new algorithms outperform NSGA-II. Length-unconstrained layouts provide up to 5.9% PO improvements against the baseline. When limited to 20 km long, the obtained layouts provide up to 2.4% PO increase and 62% CL decrease. These improvements are respectively 10 and 3 times bigger than previous results obtained with the fixed area. When deriving a localized utility function, the cost of energy is reduced up to 2.7% against the baseline.

Optimizing locations of waste transfer stations in rural areas

Many studies have considered the location of rural waste transfer stations, but most have omitted the impact of transportation network conditions. Traffic accessibility must be considered in optimizing the location of rural waste transfer stations, which is an important difference from the location of rural waste transfer stations. On the basis of previous studies, this study will consider the impact of traffic network on the optimization locations of waste transfer station in the rural areas. The objective of this study was to ensure the minimum Euclidean distance between the waste transfer station and the population center is the maximum, minimize the garbage transportation cost of each population center, construction costs for waste transfer stations, construction and upgrade costs for roads on a traffic network. A multi-objective facility location-network design model and an improved multi-objective simulated annealing algorithm was used to solve the problem. A detailed practical case study was used to illustrate the application of the proposed mathematical model. The results show that transportation network plays an important role in facility location optimization, and the improvement of traffic network conditions can greatly reduce waste transportation costs.

When, where, and how to intervene? Trade-offs between time and costs in coastal nutrient management

Policies and regulations designed to address nutrient pollution in coastal waters are often complicated by delays in environmental and social systems. Social and political inertia may delay implementation of cleanup projects, and even after the best nutrient pollution management practices are developed and implemented, long groundwater travel times may delay the impact of inland or upstream interventions. These delays and the varying costs of nutrient removal alternatives used to meet water quality goals combine to create a complex dynamic decision problem with trade-offs about when, where, and how to intervene. We use multi-objective optimization to quantify the trade-offs between costs and minimizing the time to meet in-bay nutrient reduction goals represented as a Total Maximum Daily Load (TMDL). We calculate the impact of using in-bay (in-situ) nutrient removal through shellfish aquaculture relative to waiting for traditional source control to be implemented. We apply these methods to the Three Bays Watershed in Cape Cod, Massachusetts. In gross benefit terms, not accounting for any social costs, this equates to an average value of 37¢ (2035 TMDL target date) and 11¢ (2060 TMDL target date) per animal harvested over the plan implementation period. Our results encourage the consideration of alternative and in-situ approaches to tackle coastal pollution while traditional source control is implemented and its effects realized over time.

From Prediction to Prescription: Evolutionary Optimization of Nonpharmaceutical Interventions in the COVID-19 Pandemic

Several models have been developed to predict how the COVID-19 pandemic spreads, and how it could be contained with nonpharmaceutical interventions, such as social distancing restrictions and school and business closures. This article demonstrates how evolutionary AI can be used to facilitate the next step, i.e., determining most effective intervention strategies automatically. Through evolutionary surrogate-assisted prescription, it is possible to generate a large number of candidate strategies and evaluate them with predictive models. In principle, strategies can be customized for different countries and locales, and balance the need to contain the pandemic and the need to minimize their economic impact. Early experiments suggest that workplace and school restrictions are the most important and need to be designed carefully. They also demonstrate that results of lifting restrictions can be unreliable, and suggest creative ways in which restrictions can be implemented softly, e.g., by alternating them over time. As more data becomes available, the approach can be increasingly useful in dealing with COVID-19 as well as possible future pandemics.

Optimal Siting and Sizing of Distributed Generators by Strawberry Plant Propagation Algorithm

Increasing the unplanned penetration of Distributed Generators (DGs) has spurred active and reactive power losses in the distribution system. This article suggests using a novel Strawberry Plant Propagation Algorithm (SPPA) for planning the placement of the DGs with the aim of reducing the network (active) power losses and improving the overall voltage profile. The proposed method (SPPA) has been tested on 33 and 69 node radial systems in MATLAB. A cost analysis was also performed and compared with other contemporary methods. The results for the considered variables show the significance of the proposed method in comparison to various other counterparts, including the Mine Blast Algorithm and Particle Swarm Optimization.

Using Decision Science for Monitoring Threatened Western Snowy Plovers to Inform Recovery

Simple Summary

We developed a decision-analysis evaluation of a suite of nine alternative strategies for monitoring federally Threatened populations of Western Snowy Plovers (Charadrius nivosus nivosus) along the Pacific Coast, US. The species is increasing in numbers as a result of successful recovery plan implementation efforts, and is no longer feasible to conduct absolute censuses of birds and nests, as well as track productivity, fate, and predation events at every nest. What is needed is a statistically sound and economically feasible sampling approach to continue monitoring plover populations and informing management decisions that advance recovery for the species. We convened an eight-person technical team of plover monitoring experts to score the nine alternative strategies on a set of six categories of monitoring objectives such as maximizing the accuracy of determining the adult population size. Scoring consisted of ordinal scales of performance measures related to the recovery criteria for the species, and to other criteria related to monitoring reporting. We calculated overall scores among the team members, and explored how different objective weights influenced which monitoring strategies were best. Several monitoring strategies stood out as having the highest utility, depending on the importance given to cost, which we subsequently conveyed to the US Fish and Wildlife Service, responsible for monitoring as well as for consideration when choosing a standard monitoring sampling strategy throughout all the plover recovery units.

Abstract

Western Snowy Plovers (Charadrius nivosus nivosus) are federally listed under the US Endangered Species Act as Threatened. They occur along the US Pacific coastline and are threatened by habitat loss and destruction and excessive levels of predation and human disturbance. Populations have been monitored since the 1970s for distribution, reproduction, and survival. Since the species was federally listed in 1993 and a recovery plan was approved under the US Fish and Wildlife Service in 2007, recovery actions have resulted in growing populations with increased presence at breeding and wintering sites throughout their Pacific Coast range. This success has created logistical challenges related to monitoring a recovering species and a need for identifying and instituting the best monitoring approach given recovery goals, budgets, and the likelihood of monitoring success. We devised and implemented a structured decision analysis to evaluate nine alternative monitoring strategies. The analysis included inviting plover biologists involved in monitoring to score each strategy according to a suite of performance measures. Using multi-attribute utility theory, we combined scores across the performance measures for each monitoring strategy, and applied weighted utility values to show the implications of tradeoffs and find optimal decisions. We evaluated four scenarios for weighting the monitoring objectives and how risk attitude affects optimal decisions. This resulted in identifying six strategies that best meet recovery needs and were Pareto optimal for cost-effective monitoring. Results were presented to the US Fish and Wildlife Service, responsible for monitoring as well as for consideration to ensure consistent monitoring methods across the species’ range. Our use of structured decision-making can be applied to cases of other species once imperiled but now on the road to recovery.

Matching Optimization of a Mixed Flow Pump Impeller and Diffuser Based on the Inverse Design Method

When considering the interaction between the impeller and diffuser, it is necessary to provide logical and systematic guidance for their matching optimization. In this study, the goal was to develop a comprehensive matching optimization strategy to optimize the impeller and diffuser of a mixed flow pump. Some useful tools and methods, such as the inverse design method, computational fluid dynamics (CFD), design of experiment, surrogate model, and optimization algorithm, were used. The matching optimization process was divided into two steps. In the first step, only the impeller was optimized. Thereafter, CFD analysis was performed on the optimized impeller to get the circulation and flow field distribution at the outlet of the impeller. In the second step of optimization, the flow field and circulation distribution at the inlet of the diffuser were set to be the same as the optimized impeller outlet. The results show that the matching optimization strategy proposed in this study is effective and can overcome the shortcomings of single-component optimization, thereby further improving the overall optimization effect. Compared with the baseline model, the pump efficiency of the optimized model at 1.2Qdes, 1.0Qdes, and 0.8Qdes is increased by 6.47%, 3.68%, and 0.82%, respectively.

Application of TOPSIS Approach to Multi-Criteria Selection of Wind Turbines for On-Shore Sites

The remarkable developments in renewable energy at the global scale have paved the way for a sustainable future and cleaner environment. As a fundamental element of this renewable energy revolution, wind energy has received tremendous attention worldwide. In order to harness maximal energy from a wind farm, one crucial decision is the selection of a turbine that is most compatible with the geographical and topographical characteristics of the location under consideration. This selection mechanism ideally considers multiple conflicting decision criteria—i.e., an improvement in one criterion negatively impacts one or more other criteria. Therefore, a tradeoff solution is desired in which the selection criteria are simultaneously optimized to the best achievable limits. Considering the above observations, this paper proposes a TOPSIS (the technique for order of preference by similarity to ideal solution)-based wind turbine selection approach. The problem is modeled as a multi-criteria decision problem while considering hub height, wind speed, percentages of zero and rated output, and annual energy production as the decision criteria. A case study is shown for data collected from two potential sites in northern and north-western Saudi Arabia. Fifteen turbines with rated capacities ranging from 1.5 to 3 MW from various manufacturers were evaluated. Results indicate that the TOPSIS approach was effective in identifying the best turbines for each site. Furthermore, the proposed approach helped in identifying the similarities in the behavior of turbines for the two sites. A comparison of the TOPSIS approach with other multi-criteria decision-making techniques proved the robustness of the method.

Robust Design Optimization and Emerging Technologies for Electrical Machines: Challenges and Open Problems

The bio-inspired algorithms are novel, modern, and efficient tools for the design of electrical machines. However, from the mathematical point of view, these problems belong to the most general branch of non-linear optimization problems, where these tools cannot guarantee that a global minimum is found. The numerical cost and the accuracy of these algorithms depend on the initialization of their internal parameters, which may themselves be the subject of parameter tuning according to the application. In practice, these optimization problems are even more challenging, because engineers are looking for robust designs, which are not sensitive to the tolerances and the manufacturing uncertainties. These criteria further increase these computationally expensive problems due to the additional evaluations of the goal function. The goal of this paper is to give an overview of the widely used optimization techniques in electrical machinery and to summarize the challenges and open problems in the applications of the robust design optimization and the prospects in the case of the newly emerging technologies.

Structured Decision Making to Meet a National Water Quality Mandate

Water quality criteria are necessary to ensure protection of ecological and human health conditions, but compliance can require complex decisions. We use structured decision making to consider multiple stakeholder objectives in a water quality management process, with a case study in the Three Bays watershed on Cape Cod, Massachusetts. We set a goal to meet or exceed a nitrogen load reduction target for the watershed and four key objectives: minimizing economic costs of implementing management actions, minimizing the complexity of permitting management actions, maximizing stakeholder acceptability of the management actions, and maximizing the provision of ecosystem services (recreational opportunity, erosion and flood control, socio-cultural amenity). We used multi-objective optimization and sensitivity analysis to generate many possible solutions that implement different combinations of nitrogen-removing management actions and reflect tradeoffs between the objectives. Results show that technological advances in controlling household nitrogen sources could provide lower cost solutions and positive impacts to ecosystem services. Although this approach is demonstrated with Cape Cod data, the decision-making process is not specific to any watershed and could be easily applied elsewhere.

Putting prototypes in place

It has recently been proposed that natural concepts are those represented by the cells of an optimally partitioned similarity space. In this proposal, optimal partitioning has been defined in terms of rational design criteria, criteria that a good engineer would adopt if asked to develop a conceptual system. It has been argued, for instance, that convexity should rank high among such criteria. Other criteria concern the possibility of placing prototypes such that they are both similar to the items they represent-each prototype ought to be representative-and dissimilar to each other: the prototypes ought to be contrastive. Parts of this design proposal are already supported by evidence. This paper reports results of a new study meant to address parts still lacking in empirical support. In particular, it presents data concerning color similarity space which indicate that color prototypes are indeed located such that they trade off optimally between being representative and being contrastive.

Multi-Objective Optimization of Friction Stir Spot-Welded Parameters on Aluminum Alloy Sheets Based on Automotive Joint Loads

By controlling various friction stir spot-welded (FSSW) factors, two base sheets AA 5052-H32 and 6061-T6 were selected to bond similar and dissimilar metal joints while considering dissimilar configuration orders. The effects of weld parameters on the sheer strength and peel strength were separately developed into empirical models utilizing the integrated central composite matrix design and response surface methodology (RSM). Meanwhile, the finite element (FE) analysis of the multi-axis load-bearing characteristics for automotive solder joints during service was carried out. As a result, the weights of the shear and axial stress, accounting for 90.5% and 9.5% respectively, were employed to restrict the relationship between multiple target properties, and the resulting security strength was applied to determine the feasible domain in subsequent parametric optimization. In order to enable the optimal multi-axis capacities in accordance with the load mode, the genetic algorithm NSGA-II was chosen to compute the Pareto front and further determine the best compromise solutions. The obtained optimums corresponding to each joining condition were validated by confirmation runs, indicating that this coupled multi-objective optimization approach based on working conditions was beneficial to the targeted improvement of post-weld mechanical properties.

Hybrid Imperialist Competitive and Grey Wolf Algorithm to Solve Multiobjective Optimal Power Flow with Wind and Solar Units

The optimal power flow (OPF) module optimizes the generation, transmission, and distribution of electric power without disrupting network power flow, operating limits, or constraints. Similarly to any power flow analysis technique, OPF also allows the determination of system’s state of operation, that is, the injected power, current, and voltage throughout the electric power system. In this context, there is a large range of OPF problems and different approaches to solve them. Moreover, the nature of OPF is evolving due to renewable energy integration and recent flexibility in power grids. This paper presents an original hybrid imperialist competitive and grey wolf algorithm (HIC-GWA) to solve twelve different study cases of simple and multiobjective OPF problems for modern power systems, including wind and photovoltaic power generators. The performance capabilities and potential of the proposed metaheuristic are presented, illustrating the applicability of the approach, and analyzed on two test systems: the IEEE 30 bus and IEEE 118 bus power systems. Sensitivity analysis has been performed on this approach to prove the robustness of the method. Obtained results are analyzed and compared with recently published OPF solutions. The proposed metaheuristic is more efficient and provides much better optimal solutions.

Solving multi-objective water management problems using evolutionary computation

Water as a resource is becoming increasingly more valuable given the changes in global climate. In an agricultural sense, the role of water is vital to ensuring food security. Therefore the management of it has become a subject of increasing attention and the development of effective tools to support participative decision-making in water management will be a valuable contribution. In this paper, evolutionary computation techniques and Pareto optimisation are incorporated in a model-based system for water management. An illustrative test case modelling optimal crop selection across dry, average and wet years based on data from the Murrumbidgee Irrigation Area in Australia is presented. It is shown that sets of trade-off solutions that provide large net revenues, or minimise environmental flow deficits can be produced rapidly, easily and automatically. The system is capable of providing detailed information on optimal solutions to achieve desired outcomes, responding to a variety of factors including climate conditions and economics.

A New Algorithm Using the Non-Dominated Tree to Improve Non-Dominated Sorting

Non-dominated sorting is a technique often used in evolutionary algorithms to determine the quality of solutions in a population. The most common algorithm is the Fast Non-dominated Sort (FNS). This algorithm, however, has the drawback that its performance deteriorates when the population size grows. The same drawback applies also to other non-dominating sorting algorithms such as the Efficient Non-dominated Sort with Binary Strategy (ENS-BS). An algorithm suggested to overcome this drawback is the Divide-and-Conquer Non-dominated Sort (DCNS) which works well on a limited number of objectives but deteriorates when the number of objectives grows. This article presents a new, more efficient algorithm called the Efficient Non-dominated Sort with Non-Dominated Tree (ENS-NDT). ENS-NDT is an extension of the ENS-BS algorithm and uses a novel Non-Dominated Tree (NDTree) to speed up the non-dominated sorting. ENS-NDT is able to handle large population sizes and a large number of objectives more efficiently than existing algorithms for non-dominated sorting. In the article, it is shown that with ENS-NDT the runtime of multi-objective optimization algorithms such as the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) can be substantially reduced.