Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Multiobjective optimization using dynamic neighborhood particle swarm optimization
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
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
Multi-objective particle swarm optimization on computer grids
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Multi-objective genetic algorithms: Problem difficulties and construction of test problems
Evolutionary Computation
GSA: A Gravitational Search Algorithm
Information Sciences: an International Journal
A non-dominated sorting particle swarm optimizer for multiobjective optimization
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
An improved multi-objective particle swarm optimizer for multi-objective problems
Expert Systems with Applications: An International Journal
Improving PSO-Based multi-objective optimization using crowding, mutation and ∈-dominance
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Multi-objective particle swarm optimization based on minimal particle angle
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
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
PSO-Based Multiobjective Optimization With Dynamic Population Size and Adaptive Local Archives
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Expert Systems with Applications: An International Journal
Fuzzy linear regression based on Polynomial Neural Networks
Expert Systems with Applications: An International Journal
A novel chemistry based metaheuristic optimization method for mining of classification rules
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Democratic PSO for truss layout and size optimization with frequency constraints
Computers and Structures
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In this paper a new hybrid method is proposed for multi-objective optimization problem. In multi-objective particle swarm optimization methods, selecting the global best particle for each particle of the population from a set of Pareto-optimal solutions has a great impact on the convergence and diversity of solutions. Here, this problem is solved by incorporating charged system search method into the search process of the particle swarm optimization algorithm. In this approach, each particle is guided by its personal best and also resultant force which acted on this particle. This force is the consequence of the attraction field which is created around each archive member, where the magnitude of this force is related to the charge magnitude of the particles and also the distance between them. Each particle is guided by just archive members, which are located in the same region of the objective space as this particle. The proposed method is examined for different test functions and the results are compared to the results of three state-of-art multi-objective algorithms.