Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Fundamentals of Computational Swarm Intelligence
Fundamentals of Computational Swarm Intelligence
Evolutionary algorithms for constrained parameter optimization problems
Evolutionary Computation
A particle swarm optimizer for constrained numerical optimization
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
A novel particle swarm optimization for constrained optimization problems
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Unified particle swarm optimization for solving constrained engineering optimization problems
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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In this paper, the behavior of different Particle Swarm Optimization (PSO) variants is analyzed when solving a set of well-known numerical constrained optimization problems. After identifying the most competitive one, some improvements are proposed to this variant regarding the parameter control and the constraint-handling mechanism. Furthermore, the on-line behavior of the improved PSO and some of the most competitive original variants are studied. Two performance measures are used to analyze the capabilities of each PSO to generate feasible solutions and to improve feasible solutions previously found i.e. how able is to move inside the feasible region of the search space. Finally, the performance of this improved PSO is compared against state-of-the-art PSO-based algorithms. Some conclusions regarding the behavior of PSO in constrained search spaces and the improved results presented by the modified PSO are given and the future work is established.