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A new improved version of an Intelligent Particle Swarm Optimization (IPSO) algorithm, is proposed and applied for the design of a Superconducting Magnetic Energy Storage device. IPSO offers intelligence to PSO particles by using concepts such as: learning from group experiences, local landscape models based on virtual neighbors and successful behavior parameters. The improvements proposed refer,...
In this study, parallel particle swarm optimization algorithm has been investigated as regards the impact of the problem properties on the execution time. Two major factors affect the performance of parallel evolutionary algorithms: the population size and the problem dimension. In this study, five well-know benchmark functions have been applied with different dimensions. Then, these functions have...
In this study, a method for solution of the multi-objective optimization problems via the desirability function aided particle swarm optimization has been proposed. The desirability function has been applied for normalization of each objective and then aggregation to a single objective. The geometric mean of the desirability values regarding each objective has been calculated as a part of the method...
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