Enhanced superposition determination for weighted superposition attraction algorithm
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METHODOLOGIES AND APPLICATION
Enhanced superposition determination for weighted superposition attraction algorithm Adil Baykasog˘lu1 • S¸ener Akpinar1
Ó Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract This paper argues the efficiency enhancement study of a recent meta-heuristic algorithm, WSA, by modifying one of its operators, superposition (target point) determination procedure. The original operator is based on the weighted vector summation and has some potential disadvantages with regard to domain of the decision variables such that determining a superposition out of the search space. Such potential disadvantages may cause WSA to behave as a random search and result in an unsatisfactory performance for some problems. In order to eliminate such potential disadvantages, we propose a new superposition determination procedure for the WSA algorithm. Thus, the mWSA algorithm will be able to behave more consistent during its search and its robustness will improve significantly in comparison to its original version. The mWSA algorithm is compared against the WSA algorithm and some other algorithms taken from the existing literature on both the constrained and unconstrained optimization problems. The experimental results clearly indicate that the mWSA algorithm is an improvement for the original WSA algorithm, and also prove that the mWSA algorithm is more robust and consistent search procedure in solving complex optimization problems. Keywords WSA algorithm Superposition principle Performance enhancement Functional optimization
1 Introduction According to Darwin’s Origin of Species, ‘‘it is not the strongest nor the most intelligent who will survive but those who can best manage change’’ (Megginson 1963). This statement underlines the crucial effect of reacting against changes when occurred. The motto, having the ability to manage changes, might also be acceptable for the artificial systems in addition to humankind and organisms. As artificial systems, meta-heuristic algorithms can tackle almost all optimization problems due to their adaptable structure. However, this property is not adequate by itself in order to conclude that any meta-heuristic algorithm has the ability to cope with changes during the exploration of the search space of any optimization problem. In order to make such a claim, a meta-heuristic algorithm must have
Communicated by V. Loia. & Adil Baykasog˘lu [email protected] 1
Department of Industrial Engineering, Faculty of Engineering, Dokuz Eylu¨l University, Izmir, Turkey
the ability to analyse the information at any time and guide its search according to this information. The WSA algorithm (Baykasog˘lu and Akpinar 2015, 2017) has such an ability, since the WSA algorithm uses the superposition principle during its search. As optimization is a dynamic process, the information determined during this process changes over time, and therefore, the WSA algorithm repeatedly superposes the determined information when the
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