gravitational evaluation algorithm for global optimization problem

This work proposes a new metaheuristic technique that combines differential evolution (de) with gravity search in a consistent manner. swarm intelligence benefits and the concept of tensile strength between two particles are combined to suggest superior meta-heuristic approaches for limitless optimization issues. the goal of this paper is to create a new algorithm that overcomes the shortcomings of the gravitational search algorithm by leveraging the advantages of the differential evolution algorithm in expanding search areas, overcoming early convergence problems, and improving the attractive algorithm’s ability to converge towards the optimum. the gsa algorithm has been utilized in a search-oriented algorithm, whereas the differential evolution algorithm is causing a high level of diversification in society, which leads to the establishment of search regions for the gsa algorithm. the effectiveness of the suggested approach was evaluated by solving a collection of 30 real-parameter numerical optimization problems that were presented at ieee-cec 2014. the findings are compared to 5 state-of-the-art unconstrained problem algorithms and 6 state-of-the-art unconstrained problem algorithms. the winner methods were also deduced from the results using the wilcoxon signed test.