Open AccessAn improved multi-objective gravitational search algorithm
Author(s) -
Yuhong Guo,
Teng Luo,
Hongqian Cao
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1978/1/012029
Subject(s) - sorting , mathematical optimization , algorithm , particle swarm optimization , benchmark (surveying) , best first search , genetic algorithm , convergence (economics) , search algorithm , computer science , heuristic , gravitational search algorithm , guided local search , beam search , mathematics , geodesy , economic growth , economics , geography
Multi-objective search algorithm is a common optimization tool to deal with complex multi-objective problems, such as Multiple Objectives Particle Swarm Optimization (MOPSO) and Non dominated Sorting Genetic Algorithm-II (NSGA-II). Gravitational Search Algorithm (GSA) is a new heuristic evolutionary algorithm, which is based on the Newtonian gravity and the laws of motion to search optimal solutions. Some of agents have bigger mass, so other smaller mass agents are affected easily to fall into local optimization. In order to improve the search ability of the algorithm, this paper proposes an Improved Multi-Objective Gravitational Search Algorithm (IMOGSA). The proposed method uses the fast non-dominated sorting strategy and crowding distance of NSGA-II, and uses Sine Cosine Algorithm (SCA). Using strategy of NSGA-II is to reduce the complexity of the algorithm, in addition, using SCA is to improve the convergence and distribution of IMOGSA by improving the weight of acceleration. Finally, the proposed method has been compared with other well-known heuristic search methods by using some benchmark functions.