Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
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
Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II
IEEE Transactions on Evolutionary Computation
Dynamic multiple swarms in multiobjective particle swarm optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Nonlinear analysis and optimal design of structures via force method and genetic algorithm
Computers and Structures
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Structural and Multidisciplinary Optimization
Improving PSO-Based multi-objective optimization using crowding, mutation and ∈-dominance
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Evolutionary algorithms and gradient search: similarities anddifferences
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
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
IEEE Transactions on Evolutionary Computation
PSO-Based Multiobjective Optimization With Dynamic Population Size and Adaptive Local Archives
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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In this paper, a new multi-objective optimization method is proposed to solve large scale structural problems in continuous search space. This method is based on the recently developed algorithm, so called charged system search (CSS), which has been used for single objective optimization. In this study the aim is to develop a multi-objective optimization algorithm with higher convergence rate compared to the other well-known methods to enable to deal with multi-modal optimization problems having many design variables. In this method, the CSS algorithm is utilized as a search engine in combination with clustering and particle regeneration procedures. The proposed method is examined for four mathematical functions and two structural problems, and the results are compared to those of some other state-of-art approaches.