and Passino proposed the bacterial foraging optimization (BFO), inspired by the group foraging behavior of bacteria such as E. Storn and Price proposed a differential evolution (DE). In addition to them, scholars have shown great interest in proposing new intelligent approaches. Those are two most famous SI-based optimization algorithms. Kennedy and Eberhart proposed a particle swarm optimization (PSO) method based on bird flocking. Well-known examples of SI include ant colonies, bird flocking, animal herding, bacterial growth, and fish schooling.ĭorigo proposed an ant colony optimization (ACO) method based on ant colony. The agents’ real behaviors are local, and to a certain degree random however, interactions between such agents lead to the emergence of “intelligent” global behavior, which is unknown to the individual agents. There is no centralized control structure dictating how individual agents should behave. The agents in a SI system follow very simple rules. The inspiration often comes from nature, especially biological systems. Typical SI systems consist of a population of simple agents or boids interacting locally with one another and with their environment. It researches the collective behavior of decentralized, self-organized systems, natural or artificial. CI includes artificial neural network (ANN), fuzzy logic, and evolutionary computation (EC). It is a set of nature-inspired computational methodologies and approaches to address complex real-world problems to which traditional approaches are ineffective or infeasible. Currently popular approaches of AI include traditional statistical methods, traditional symbolic AI, and computational intelligence (CI). AI research is highly technical and specialized and is deeply divided into subfields that often fail to communicate with each other. It is defined as “the study and design of intelligent agents”, where an intelligent agent represents a system that perceives its environment and takes action that maximizes its success chance. IntroductionĪrtificial intelligence (AI) is the intelligence exhibited by machines. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms. On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). This survey presented a comprehensive investigation of PSO. It is now one of the most commonly used optimization techniques. Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995.
0 kommentar(er)
