Book Chapter10.1007/978-3-642-31837-5_7
Constrained Multi-objective Particle Swarm Optimization Algorithm
Yue-lin Gao,Min Qu +1 more
- 25 Jul 2012
- pp 47-55
9
TL;DR: Numerical experiments show the effectiveness of the proposed CMPSO algorithm, which employs particle swarm optimization algorithm and Pareto neighborhood crossover operation to generate new population.
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Abstract: A particle swarm optimization for solving constrained multi-objective optimization problem was proposed (CMPSO). In this paper, the main idea is the use of penalty function to handle the constraints. CMPSO employs particle swarm optimization algorithm and Pareto neighborhood crossover operation to generate new population. Numerical experiments are compared with NSGA-II and MOPSO on three benchmark problems. The numerical results show the effectiveness of the proposed CMPSO algorithm.
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TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
35K
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11K
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