Nature Inspired Computing: An Overview and Some Future Directions

This paper presents an overview of significant advances made in the emerging field of nature-inspired computing (NIC) with a focus on the physics- and biology-based approaches and algorithms. A parallel development in the past two decades has been the emergence of the field of computational intelligence (CI) consisting primarily of the three fields of neural networks, evolutionary computing and fuzzy logic. It is observed that NIC and CI intersect. The authors advocate and foresee more cross-fertilisation of the two emerging fields.

Spiral Dynamics Algorithm

The spiral has universal appeal and a mysterious resonance with the human spirit. It is intriguing and beautiful. The spiral pattern is found extensively in nature, encoded into plants, animals, weather phenomena, the earth, and galaxies around us. The paper starts with a mathematical representation of the spiral phenomena. Next, 2-dimensional and n-dimensional Spiral Dynamics Optimization (SpDO) algorithms are presented followed by their variants such as adaptive SpDO and hybrid SpDO. Finally, applications to engineering problems are reviewed including a flexible robot manipulator system, training of artificial neural networks, the Combined Economic and Emission Dispatch (CEED) optimization problem in power system operations, and clustering in data mining.

Water Drop Algorithms

This article presents a review of a novel nature-inspired adaptive optimization algorithm known as Water Drop Algorithm (WDA) which imitates the dynamics of river systems and the behavior of water drops when they are moving in a river system such as the variation of velocity, the change of sediment/soil in the river bed, the change of direction of the flow, and so on. Recent improved, modified, and adaptive WDAs are examined in this paper. Various applications of WDA are described briefly including the vehicle routing problem, economic load dispatch problem, economic and emission dispatch problem, optimal data aggregation tree in wireless networks, reactive power dispatch problem service selection, and reservoir operation.

INTELLIGENT MULTIAGENT COORDINATION BASED ON REINFORCEMENT HIERARCHICAL NEURO-FUZZY MODELS

This paper presents the research and development of two hybrid neuro-fuzzy models for the hierarchical coordination of multiple intelligent agents. The main objective of the models is to have multiple agents interact intelligently with each other in complex systems. We developed two new models of coordination for intelligent multiagent systems, which integrates the Reinforcement Learning Hierarchical Neuro-Fuzzy model with two proposed coordination mechanisms: the MultiAgent Reinforcement Learning Hierarchical Neuro-Fuzzy with a market-driven coordination mechanism (MA-RL-HNFP-MD) and the MultiAgent Reinforcement Learning Hierarchical Neuro-Fuzzy with graph coordination (MA-RL-HNFP-CG). In order to evaluate the proposed models and verify the contribution of the proposed coordination mechanisms, two multiagent benchmark applications were developed: the pursuit game and the robot soccer simulation. The results obtained demonstrated that the proposed coordination mechanisms greatly improve the performance of the multiagent system when compared with other strategies.