Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art
Evolutionary Computation
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
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
A MOPSO algorithm based exclusively on pareto dominance concepts
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
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
Multiswarms, exclusion, and anti-convergence in dynamic environments
IEEE Transactions on Evolutionary Computation
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To solve problems such as low global search capability and insufficient diversity of Pareto optimal set existing in MOPSO, a multiobjective particle swarm optimization algorithm based on crowding distance sorting is proposed. An external population is preserved to store the non-dominated individuals during the evolution process. The shrink of the external population is achieved based on individuals' crowding distance sorting by descending order, which deletes the redundant individuals in the crowding area. An individual with relatively big crowding distance is selected as the global best to lead the particles evolving to the disperse region. The dominant relation between individuals is compared with the constraint Pareto dominance to embody the constraints without external parameters. The experiments of six standard unconstrained test problems illustrate that the new algorithm is competitive with NSGA-II and SPEA2 in terms of converging to the true Pareto front and maintaining the diversity of the population. The effectiveness of the algorithm for constraint problems is proved by solving three constraint test problems. Moreover, the best value ranges of mutation rate and inertia weight are analyzed by numerical experiments to guarantee the steady convergence of the algorithm.