Amin Ghodousian; Mahdi Mollakazemiha; Noushin Karimian
Abstract
This paper proposes a novel population-based meta-heuristic optimization algorithm, called Perfectionism SearchAlgorithm (PSA), which is based on the psychological aspects of perfectionism. The PSA algorithm takes inspiration from one of the most popular model of perfectionism, which was proposed by ...
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This paper proposes a novel population-based meta-heuristic optimization algorithm, called Perfectionism SearchAlgorithm (PSA), which is based on the psychological aspects of perfectionism. The PSA algorithm takes inspiration from one of the most popular model of perfectionism, which was proposed by Hewitt and Flett. During each iteration of the PSA algorithm, new solutions are generated by mimicking different types and aspects of perfectionistic behavior. In order to have a complete perspective on the performance of PSA, the proposed algorithm is tested with various nonlinear optimization problems, through selection of 35 benchmark functions from the literature. The generated solutions for these problems, were also compared with 11 well-known meta-heuristics which had been applied to many complex andpractical engineering optimization problems. The obtained results confirm the high performance of the proposedalgorithm in comparison to the other well-known algorithms.
Mina Moosapour; Ahmad Bagheri; Mohammad Javad Mahmoodabadi
Abstract
The imperialist competitive algorithm (ICA) is developed based on the socio-political process of imperialist competitions. It is an efficient approach for single-objective optimization problems. However, this algorithm fails to optimize multi-objective problems (MPOs) with conflicting objectives. This ...
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The imperialist competitive algorithm (ICA) is developed based on the socio-political process of imperialist competitions. It is an efficient approach for single-objective optimization problems. However, this algorithm fails to optimize multi-objective problems (MPOs) with conflicting objectives. This paper presents a modification of the ICA to different multi-objective problems. To improve the algorithm performance and adapt to the characteristics of MOPs, the Sigma method was used to establish the initial empires, the weighted sum approach (WSum) was employed for empire competition, and an adaptive elimination approach was used for external archiving strategy. the results indicated that the suggested algorithm had a higher performance compared to other algorithms based on diversity and convergence characteristics.
