Multi-Objective Volleyball Premier League algorithm

An efficient multi-objective parrot optimizer for global and engineering optimization problems

The Parrot Optimizer (PO) has recently emerged as a powerful algorithm for single-objective optimization, known for its strong global search capabilities. This study extends PO into the Multi-Objective Parrot Optimizer (MOPO), tailored for multi-objective optimization (MOO) problems. MOPO integrates an outward archive to preserve Pareto optimal solutions, inspired by the search behavior of Pyrrhura Molinae parrots. Its performance is validated on the Congress on Evolutionary Computation 2020 (CEC'2020) multi-objective benchmark suite. Additionally, extensive testing on four constrained engineering design challenges and eight popular confined and unconstrained test cases proves MOPO's superiority. Moreover, the real-world multi-objective optimization of helical coil springs for automotive applications is conducted to depict the reliability of the proposed MOPO in solving practical problems. Comparative analysis was performed with seven recently published, state-of-the-art algorithms chosen for their proven effectiveness and representation of the current research landscape-Improved Multi-Objective Manta-Ray Foraging Optimization (IMOMRFO), Multi-Objective Gorilla Troops Optimizer (MOGTO), Multi-Objective Grey Wolf Optimizer (MOGWO), Multi-Objective Whale Optimization Algorithm (MOWOA), Multi-Objective Slime Mold Algorithm (MOSMA), Multi-Objective Particle Swarm Optimization (MOPSO), and Non-Dominated Sorting Genetic Algorithm II (NSGA-II). The results indicate that MOPO consistently outperforms these algorithms across several key metrics, including Pareto Set Proximity (PSP), Inverted Generational Distance in Decision Space (IGDX), Hypervolume (HV), Generational Distance (GD), spacing, and maximum spread, confirming its potential as a robust method for addressing complex MOO problems.

MOCOVIDOA: a novel multi-objective coronavirus disease optimization algorithm for solving multi-objective optimization problems

A novel multi-objective Coronavirus disease optimization algorithm (MOCOVIDOA) is presented to solve global optimization problems with up to three objective functions. This algorithm used an archive to store non-dominated POSs during the optimization process. Then, a roulette wheel selection mechanism selects the effective archived solutions by simulating the frameshifting technique Coronavirus particles use for replication. We evaluated the efficiency by solving twenty-seven multi-objective (21 benchmarks & 6 real-world engineering design) problems, where the results are compared against five common multi-objective metaheuristics. The comparison uses six evaluation metrics, including IGD, GD, MS, SP, HV, and delta p (Δ P ). The obtained results and the Wilcoxon rank-sum test show the superiority of this novel algorithm over the existing algorithms and reveal its applicability in solving multi-objective problems.

A Wolf Pack Optimization Algorithm Using RASGS and GBA for Multi-Modal Multi-Objective Problems

To address multi-modal multi-objective problems (MMOPs), this paper proposes a wolf pack optimization algorithm using random adaptive-shrinking grid search (RASGS) and raid towards global best archive (GBA) for MMOPs. Firstly, RASGS with logical symmetry was adopted to enhance the exploitation of the algorithm in the local area as well as locate a larger number of Pareto-optimal solutions. Moreover, with the help of an existing sorting method composed of the non-dominated sorting scheme and special crowding distance (SCD), the GBA strategy was employed to obtain and maintain the historical global optimal solution of the population as well as induce the population to explore better solutions. The experimental results indicate that the proposed method has obvious superior performance compared with the existing related algorithms.

Obtaining Bricks Using Silicon-Based Materials: Experiments, Modeling and Optimization with Artificial Intelligence Tools

In the brick manufacturing industry, there is a growing concern among researchers to find solutions to reduce energy consumption. An industrial process for obtaining bricks was approached, with the manufacturing mix modified via the introduction of sunflower seed husks and sawdust. The process was analyzed with artificial intelligence tools, with the goal of minimizing the exhaust emissions of CO and CH4. Optimization algorithms inspired by human and virus behaviors were applied in this approach, which were associated with neural network models. A series of feed-forward neural networks have been developed, with 6 inputs corresponding to the working conditions, one or two intermediate layers and one output (CO or CH4, respectively). The results for ten biologically inspired algorithms and a search grid method were compared successfully within a single objective optimization procedure. It was established that by introducing 1.9% sunflower seed husks and 0.8% sawdust in the brick manufacturing mix, a minimum quantity of CH4 emissions was obtained, while 0% sunflower seed husks and 0.5% sawdust were the minimum quantities for CO emissions.