Co-evolutionary particle swarm optimization to solve min-max problems
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
An Improved Hybrid Multi-objective Particle Swarm Optimization Algorithm
ISICA '08 Proceedings of the 3rd International Symposium on Advances in Computation and Intelligence
Dynamic multiple swarms in multiobjective particle swarm optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A non-dominated sorting particle swarm optimizer for multiobjective optimization
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Improving PSO-Based multi-objective optimization using crowding, mutation and ∈-dominance
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Handling multiple objectives with particle swarm optimization
IEEE Transactions on Evolutionary Computation
A hybrid of genetic algorithm and particle swarm optimization for recurrent network design
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Multi-Objective Particle Swarm Optimizers (MOPSOs) easily converge to a false Pareto front. In this paper, we proposed a hybrid algorithm of MOPSO with evolutionary programming (denoted as EPMOPSO) for solving MOPs. In EPMOPSO, the neighborhood of each particle is dynamically constructed, and the velocity of each particle is adjusted by all particles in its neighborhood including itself, the best performing particle in the swarm and the elite group that is evolved using evolutionary programming. Simulation results show that EPMOPSO is able to find a much better spread of solutions and has faster convergence to true Pareto-optimal front compared with five state-of-the-art MOPSOs.