A Hybrid Artificial Grasshopper Optimization (HAGOA) Meta-Heuristic Approach: A Hybrid Optimizer For Discover the Global Optimum in Given Search Space

International Journal of Mathematical, Engineering and Management Sciences

Shaveta Rani¹  Paramjeet Singh¹  Brahm Prakash Dahiya² 

[1] Department of Computer Science and Engineering , Giani Zail Singh Campus College of Engineering and Technology, Punjab, India;Department of Computer Science and Engineering , I. K. G. Punjab Technical University, Punjab, India;

关键词: Swarm intelligence (SI);    Hybrid artificial grasshopper optimization (HAGOA);    Modified artificial bee colony (MABC);    Modify particle swarm optimization (MPSO);   

DOI  :  10.33889/IJMEMS.2019.4.2-039

来源: DOAJ

【 摘 要 】

Meta-heuristic algorithms are used to get optimal solutions in different engineering branches. Here four types of meta-heuristics algorithms are used such as evolutionary algorithms, swarm-based algorithms, physics based algorithms and human based algorithms respectively. Swarm based meta-heuristic algorithms are given more effective result in optimization problem issues and these are generated global optimal solution. Existing swarm intelligence techniques are suffered with poor exploitation and exploration in given search space. Therefore, in this paper Hybrid Artificial Grasshopper Optimization (HAGOA) meta-heuristic algorithm is proposed to improve the exploitation and exploration in given search space. HAGOA is inherited Salp swarm behaviors. HAGOA performs balancing in exploitation and exploration search space. It is capable to make chain system between exploitation and exploration phases. The efficiency of HAGOA meta-heuristic algorithm will analyze using 19 benchmarks functions from F1 to F19. In this paper, HAGOA algorithm is performed efficiency analyze test with Artificial Grasshopper optimization (AGOA), Hybrid Artificial Bee Colony with Salp (HABCS), Modified Artificial Bee Colony (MABC), and Modify Particle Swarm Optimization (MPSO) swarm based meta-heuristic algorithms using uni-modal and multi-modal functions in MATLAB. Comparison results are shown that HAGOA meta-heuristic algorithm is performed better efficiency than other swarm intelligence algorithms on the basics of high exploitation, high exploration, and high convergence rate. It also performed perfect balancing between exploitation and exploration in given search space.

【 授权许可】

Unknown