Swarm intelligence
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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In this paper, we incorporate pheromone courtship mode of biology to improve particle swarm optimizer. The particle swarm optimization technique has ever since turned out to be a competitor in the field of numerical optimization. A particle swarm optimization consists of a number of individuals refining their knowledge of the given search space. Particle swarm optimizations are inspired by particles moving around in the search space. The individuals in a particle swarm optimization thus have a position, a velocity and are denoted particles. The particle swarm optimization refines its search by attracting the particles to positions with good solutions. A new approach to particle motion in swarm optimization is developed. The living beings will release pheromone while seeking a spouse, use to attract the opposite sex to near. We tried to incorporate this kind of mode to solve the optimization problems. Preliminary simulation results show that the proposed method can solve the optimization problem with satistactory accuracy. Convergence analysis is investigated in this paper.