Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning
Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning
A Particle Swarm Algorithm for Multiobjective Design Optimization
ICTAI '06 Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence
Fundamentals of Computational Swarm Intelligence
Fundamentals of Computational Swarm Intelligence
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
MOPSO: a proposal for multiple objective particle swarm optimization
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
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Particle swarm optimization (PSO) has been widely used in multi-objective engineering design optimization where parameter selection is of prime importance. This paper proposes a multi-objective particle swarm optimizer (MOPSO) with a modified crowding factor and enhanced local search ability. A new parameter-less sharing method is introduced to estimate the density of particles' neighborhood in the search space. Initially, the proposed method determines the crowding factor of the solutions; in later stages, it effectively guides the entire swarm to converge closely to the true Pareto front. In addition, the gradient descent search method is applied. The algorithm's performance on two engineering design problems is highlighted and compared with other approaches. The obtained results demonstrate that the proposed algorithm is capable of effectively searching along the Pareto optimal front and successfully obtaining trade-off solutions for the engineering design problems.