ABSTRACT
Abstract: To make up for the deficiencies of the Harris hawk optimization algorithm (HHO) in solving multi-objective optimization problems with low algorithm accuracy, slow rate of convergence, and easily fall into the trap of local optima, a multi-strategy improved multi-objective Harris hawk optimization algorithm with elite opposition-based learning (MO-EMHHO) is proposed. First, the population is initialized by Sobol sequences to increase population diversity. Second, incorporate the elite backward learning strategy to improve population diversity and quality. Further, an external profile maintenance method based on an adaptive grid strategy is proposed to make the solution better contracted to the real Pareto frontier. Subsequently, optimize the update strategy of the original algorithm in a non-linear energy update way to improve the exploration and development of the algorithm. Finally, improving the diversity of the algorithm and the uniformity of the solution set using an adaptive variation strategy based on Gaussian random wandering. Experimental comparison of the multi-objective particle swarm algorithm (MOPSO), multi-objective gray wolf algorithm (MOGWO), and multi-objective Harris Hawk algorithm (MOHHO) on the commonly used benchmark functions shows that the MO-EMHHO outperforms the other compared algorithms in terms of optimization seeking accuracy, convergence speed and stability, and provides a new solution to the multi-objective optimization problem.
- Deb K, Agrawal S, Pratap A, A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II[J]. Lecture notes in computer science, 2000: 849-858.Google ScholarCross Ref
- Deb K, Jain H. An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints[J]. IEEE Transactions on Evolutionary Computation, 2014, 18(4):577-601.Google ScholarCross Ref
- Xue F, Sanderson A C, Graves R J. Pareto-based multi-objective differential evolution[C]//Congress on Evolutionary Computation, 2003. CEC'03. IEEE, 2003, 2: 862-869.Google Scholar
- Coello C, Lechuga M S. MOPSO: A proposal for multiple objective particle swarm optimization[C]// Congress on Evolutionary Computation. IEEE Service Center, 2002.Google Scholar
- Gong M, Jiao L, Du H, Multiobjective immune algorithm with nondominated neighbor-based selection[J]. Evolutionary computation, 2008, 16(2): 225-255.Google Scholar
- Chaharsooghi S K. An intelligent multi-colony multi-objective ant colony optimization for the 0-1 knapsack problem[J]. Proc. of IEEE/CEC, 2008, 2008.Google ScholarCross Ref
- He X S, Li N, Yang X S, Multi-objective Cuckoo Search Algorithm[J]. Journal of System Simulation, 2015,27(04):731-737.Google Scholar
- Heidari A A, Mirjalili S, Faris H, Harris hawks optimization: Algorithm and applications[J]. Future generation computer systems, 2019, 97: 849-872.Google Scholar
- Yüzge U, Kusoglu M. Multi-objective harris hawks optimizer for multiobjective optimization problems[J]. BSEU Journal of Engineering Research and Technology, 2020, 1(1): 31-41.Google Scholar
- Selim A, Kamel S, Alghamdi A S, Optimal placement of DGs in distribution system using an improved harris hawks optimizer based on single-and multi-objective approaches[J]. IEEE Access, 2020, 8: 52815-52829.Google ScholarCross Ref
- Devarapalli R, Bhattacharyya B. Optimal parameter tuning of power oscillation damper by MHHO algorithm[C]//2019 20th International conference on intelligent system application to power systems (ISAP). IEEE, 2019: 1-7.Google Scholar
- Bratley P, Fox B L. Implementing sobols quasirandom sequence generator (algorithm 659)[J]. ACM Transactions on Mathematical Software, 2003, 29(1): 49-57.Google ScholarDigital Library
- Tizhoosh, H. R . Opposition-Based Learning: A New Scheme for Machine Intelligence[C]// International Conference on International Conference on Computational Intelligence for Modelling, Control & Automation. IEEE, 2005:695-701.Google Scholar
- Peng H, Zeng Z, Deng C, Multi-strategy serial cuckoo search algorithm for global optimization[J]. Knowledge-Based Systems, 2021, 214: 106729.Google ScholarCross Ref
- Zhang Q, Zhou A, Zhao S, Multiobjective optimization test instances for the CEC 2009 special session and competition[J]. University of Essex, Colchester, UK and Nanyang technological University, Singapore, special session on performance assessment of multi-objective optimization algorithms, technical report, 2008, 264: 1-30.Google Scholar
- Yanan Sun and Gary G. Yen and Zhang Yi 0001. IGD Indicator-Based Evolutionary Algorithm for Many-Objective Optimization Problems.[J]. IEEE Trans. Evolutionary Computation, 2019, 23(2): 173-187.Google Scholar
Index Terms
- Multi-strategy Improved Multi-objective Harris Hawk Optimization Algorithm with Elite Opposition-based Learning
Recommendations
An improved multi-objective population-based extremal optimization algorithm with polynomial mutation
As a recently developed evolutionary algorithm inspired by far-from-equilibrium dynamics of self-organized criticality, extremal optimization (EO) has been successfully applied to a variety of benchmark and engineering optimization problems. However, ...
Covariance Matrix Adaptation for Multi-objective Optimization
The covariancematrix adaptation evolution strategy (CMA-ES) is one of themost powerful evolutionary algorithms for real-valued single-objective optimization. In this paper, we develop a variant of the CMA-ES for multi-objective optimization (MOO). We ...
A multi-objective improved teaching-learning based optimization algorithm (MO-ITLBO)
This paper presents an efficient multi-objective improved teaching-learning based optimization (MO-ITLBO) algorithm for solving multi-objective optimization problems. The proposed algorithm uses a grid-based approach in order to keep diversity in the ...
Comments