Student psychology based optimization algorithm: A new population based optimization algorithm for solving optimization problems. (August 2020)

• Record Type: Journal Article
• Authors: Das, Bikash; Mukherjee, V.; Das, Debapriya
• Is Part Of: Advances in engineering software. Volume 146(2020)
• Publication Date: 2020-08
• Subjects: Benchmark function -- CEC 2015 -- Global optimum solution -- Optimization algorithm -- Student psychology based optimization (SPBO); Computer-aided engineering -- Periodicals; Engineering -- Computer programs -- Periodicals; Engineering -- Software -- Periodicals; Periodicals; 620.0028553
• DOI: 10.1016/j.advengsoft.2020.102804
• Languages: English
• ISSNs: 0965-9978

Abstract

Highlights: A new student psychology based optimization (SPBO) algorithm is proposed. Effectiveness of the SPBO is demonstrated through benchmark function test. The proposed algorithm is applied to solve CEC 2015 benchmark functions. The obtained results are compared with other state-of-art algorithms. SPBO perform well in the entire test and offers faster convergence mobility. Abstract: In this article, a new metaheuristic optimization algorithm (named as, student psychology based optimization (SPBO)) is proposed. The proposed SPBO algorithm is based on the psychology of the students who are trying to give more effort to improve their performance in the examination up to the level for becoming the best student in the class. Performance of the proposed SPBO is analyzed while applying the algorithm to solve thirteen 50 dimensional benchmark functions as well as fifteen CEC 2015 benchmark problems. Results of the SPBO is compared to the performance of ten other state-of-the-art optimization algorithms such as particle swarm optimization, teaching learning based optimization, cuckoo search algorithm, symbiotic organism search, covariant matrix adaptation with evolution strategy, success-history based adaptive differential evolution, grey wolf optimization, butterfly optimization algorithm, poor and rich optimization algorithm, and barnacles mating optimizer. For fair analysis, performances of all these algorithms are analyzed based on the optimum results obtained as well as based on convergence mobility of the objective function. Pairwise and multiple comparisons are performed to analyze the statistical performance of the proposed method. From this study, it may be established that the proposed SPBO works very well in all the studied test cases and it is able to obtain an optimum solution with faster convergence mobility.