Abstract
Grey wolf Optimizer (GWO) is one of the well known meta-heuristic algorithm for determining the minimum value among a set of values. In this paper, we proposed a novel optimization algorithm called collaborative strategy for grey wolf optimizer (CSGWO). This algorithm enhances the behaviour of GWO that enhances the search feature to search for more points in the search space, whereas more groups will search for the global minimal points. The algorithm has been tested on 23 well-known benchmark functions and the results are verified by comparing them with state of the art algorithms: Polar particle swarm optimizer, sine cosine Algorithm (SCA), multi-verse optimizer (MVO), supernova optimizer as well as particle swarm optimizer (PSO). The results show that the proposed algorithm enhanced GWO behaviour for reaching the best solution and showed competitive results that outperformed the compared meta-heuristics over the tested benchmarked functions.
Highlights
A common mathematical problem in all engineering disciplines is optimization which is finding the best solutions
We propose an enhanced meta-heuristic algorithm that modifies the behavior of Grey Wolf Optimizer (GWO) algorithm by enhancing the neighborhood topology and movement strategy to find the optimal solution in the search space; by applying a collaborative strategy for Grey wolf Optimization Algorithm (CSGWO)
S. et al (2012), proposed a new hybrid particle swarm optimizer (PSO) (HPSO) to solve the problem that PSO often falls into local optima; the results have shown that HPSO has a faster convergence rate on those simple unimodal functions and superior global search ability on those multimodal functions compared to other PSO
Summary
A common mathematical problem in all engineering disciplines is optimization which is finding the best solutions. We propose an enhanced meta-heuristic algorithm that modifies the behavior of GWO algorithm by enhancing the neighborhood topology and movement strategy to find the optimal solution in the search space; by applying a collaborative strategy for Grey wolf Optimization Algorithm (CSGWO). Al-Sayyed et al, 2017 proposed novel optimization algorithm called POLARPSO that enhances the behavior of PSO and avoids the local minima problem by using a polar function to search for more points in the search space. The Collaborative strategy behaviour improves the GWO search ability at each iteration by employing multi-group search ability This improvement include more points and polar direction to the particle movement which increased the possibility to find the global minima and avoid the local minima problem. Xβ=the second best search agent Xδ=the third best search agent while (t < Max number of iterations) for each search agent Update the position of the current search agent by equation (3.*) for each search agent Update the position of the current search agent by equation (3.*) for each search agent Update the position of the current search agent by equation (3.*) end for Update a, A, and C Calculate the fitness of all search agents Update Xα, Xβ, and Xδ
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