Abstract
This paper suggests a non-dominated sorting genetic algorithm II (NSGA-II) as a multi-objective framework to construct a multi-objective optimization algorithm and uses the squirrel search algorithm (SSA) as the core evolution strategy. And a multi-objective improved squirrel search algorithm (MOSSA) is proposed. MOSSA establishes an external archive of the population to maintain the elitists in the population. The probability density is applied to limit the size of the merged population to maintain population diversity, based on roulette wheel selection. Also, this paper designs a fitness mapping evaluation according to the individual fitness value of each object. Compared with the original SSA, the generational gap is introduced to make the seasonal condition suitable for multi-objective optimization, which could keep the solution from the local con-vergence. This paper simulates MOSSA and other algorithms on multi-objective test functions to analyze the convergence and diversity of PF. It is concluded that MOSSA has a good performance in solving multi-objective problems.
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References
Jain, M., Singh, V., Rani, A.: A novel nature-inspired algorithm for optimization: squirrel search algorithm. Swarm Evol. Comput. S2210650217305229 (2018)
Wang, Y., Du, T.: A multi-objective improved squirrel search algorithm based on decomposition with external population and adaptive weight vectors adjustment. Physica A: Stat. Mech. Appl. 542, (2020)
Liang, J.J., Qu, B.Y., Suganthan, P.N.: Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization. Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China and Technical Report, Nanyang Technological University, Singapore, 635 (2013)
Gunantara, N.. A review of multi-objective optimization: methods and its applications. Cogent Eng. 5(1), 1502242 (2018)
Guo, Z., Liu, L., Yang, J.: A multi-objective memetic optimization approach for green transportation scheduling. In: 2015 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS), IEEE (2015)
Dai, M., Tang, D., Giret, A., Salido, M.A.: Multi-objective optimization for energy-efficient flexible job shop scheduling problem with transportation constraints. Robot. Comput. Integrated Manuf. 59, 143–157 (2019)
Zaro, F.R., Abido, M.A.: Multi-objective particle swarm optimization for optimal power flow in a deregulated environment of power systems. In: 2011 11th International Conference on Intelligent Systems Design and Applications, IEEE (2019)
Liu, Z., Jiang, D., Zhang, C., et al.: A novel fireworks algorithm for the protein-ligand docking on the AutoDock. Mob. Netw. Appl. 1–12, 53 (2019)
de Villiers, D.I., Couckuyt, I., Dhaene, T.: Multi-objective optimization of reflector antennas using kriging and probability of improvement. In: 2017 IEEE International Symposium on Antennas and Propagation & USNC/URSI National Radio Science Meeting, pp. 985–986. IEEE, July 2017 (2007)
Delgarm, N., Sajadi, B., Kowsary, F., Delgarm, S.: Multi-objective optimization of the building energy performance: a simulation-based approach by means of particle swarm optimization (PSO). Appl. Energy 170, 293–303 (2016)
von Lücken, C., Barán, B., Brizuela, C.: A survey on multi-objective evolutionary algorithms for many-objective problems. Comput. Optimization Appl. 1–50 (2014)
Cho, J.H., Wang, Y., Chen, R., et al.: A survey on modeling and optimizing multi-objective systems. IEEE Commun. Surv. Tutorials 19(3), 1867–1901 (2017)
Xue, B., Zhang, M., Browne, W.N.: Particle swarm optimization for feature selection in classification: a multi-objective approach. Cybern. Trans. IEEE 43(6), 1656–1671 (2013)
Zhang, Q., Li, H.: Moea/d: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11(6), 712–731 (2008)
Deb, K., Pratap, A., Agarwal, S., et al.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)
Mashwani, W.K., Salhi, A., Yeniay, O., et al.: Hybrid non-dominated sorting genetic algorithm with adaptive operators selection. Appl. Soft Comput. 56, 1–18 (2017)
Li, K., Wang, R., Zhang, T., et al.: Evolutionary many-objective optimization: a comparative study of the state-of-the-art. IEEE Access 6, 26194–26214 (2018)
Liu, Z., Zhang, C., Zhao, Q., et al.: Comparative study of evolutionary algorithms for protein-ligand docking problem on the AutoDock. International Conference on Simulation Tools and Techniques, pp. 598–607. Springer, Cham (2019)
Azzouz, R., Bechikh, S., Said, L.B.: Dynamic Multi-objective Optimization using Evolutionary Algorithms: A Survey. Recent Advances in Evolutionary Multi-objective Optimization, pp. 31–70. Springer, Cham (2017)
Bechikh, S., Elarbi, M., Said, L.B.: Many-objective Optimization using Evolutionary Algorithms: A Survey. Recent Advances in Evolutionary Multi-objective Optimization, pp. 105–137. Springer, Cham (2017)
Falcón-Cardona, J.G., Coello, C.A.C.: Indicator-based multi-objective evolutionary algorithms: a comprehensive survey. ACM Comput. Surv. (CSUR) 53(2), 1–35 (2020)
Shamshirband, S., Shojafar, M., Hosseinabadi, A.A.R., Abraham, A.: A solution for multi-objective commodity vehicle routing problem by NSGA-II. International Conference on Hybrid Intelligent Systems. IEEE (2015)
Luo, G., Wen, X., Li, H., et al.: An effective multi-objective genetic algorithm based on immune principle and external archive for multi-objective integrated process planning and scheduling. The Int. J. Adv. Manuf. Technol. 91(9–12), 3145–3158 (2017)
Gadhvi, B., Savsani, V., Patel, V.: Multi-objective optimization of vehicle passive suspension system using NSGA-II, SPEA2 and PESA-II. Procedia Technol. 2016(23), 361–368 (2016)
Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: improving the strength Pareto evolutionary algorithm. TIK-report, 103 (2001)
Wang, Y., Han, M.: Research on multi-objective multidisciplinary design optimization based on particle swarm optimization. In: 2017 Second International Conference on Reliability Systems Engineering (ICRSE). IEEE (2017)
Kaoutar, S., Mohamed, E.: Multi-criteria optimization of neural networks using multi-objective genetic algorithm. International Conference on Inteligent Systems & Computer Vision ISCV (2017)
Rosales-Perez, A., Garcia, S., Gonzalez, J.A., Coello, C.A.C., Herrera, F.: An evolutionary multiobjective model and instance selection for support vector machines with pareto-based ensembles. IEEE Trans. Evol. Comput. 21(6), 863–877 (2017)
Juang, Chia-Feng., Jeng, Tian-Lu, Chang, Yu-Cheng: An interpretable fuzzy system learned through online rule generation and multiobjective ACO with a mobile robot control application. IEEE Trans. Cybern. 46(12), 2706–2718 (2017)
Sheikholeslami, F., Navimipour, N.J.: Service allocation in the cloud environments using multi-objective particle swarm optimization algorithm based on crowding distance. Swarm and Evol. Comput. 35, 53–64 (2017)
Tian, Y., Cheng, R., Zhang, X., et al.: Diversity assessment of multi-objective evolutionary algorithms: performance metric and benchmark problems [research frontier]. IEEE Comput. Intell. Magazine 14(3), 61–74 (2019)
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Wang, X., Zhang, F., Liu, Z., Zhang, C., Zhao, Q., Zhang, B. (2021). A Novel Multi-objective Squirrel Search Algorithm: MOSSA. In: Song, H., Jiang, D. (eds) Simulation Tools and Techniques. SIMUtools 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 370. Springer, Cham. https://doi.org/10.1007/978-3-030-72795-6_15
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