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Evolutionary Multi-objective Whale Optimization Algorithm

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Intelligent Systems Design and Applications (ISDA 2018 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 941))

Abstract

Whale Optimization Algorithm (WOA) is a recently proposed metaheuristic algorithm and achieved much attention of the researchers worldwide for its competitive performance over other popular metaheuristic algorithms. As a metaheuristic algorithm, it mimics the hunting behavior of humpback whale which uses its unique spiral bubble-net feeding maneuver to search and hunt prey. The WOA has been designed to solve mono-objective problems and it shows great performance and even surplus other state of the art metaheuristics in terms of fast convergence and other performance criteria. But this such a distinctive and successful metaheuristic’s performance in dealing multi-objective problems especially in dealing with multi-objective benchmark problems has not been studied that much extent. In this paper, we developed a multi-objective version of WOA which incorporates both whale search and evolutionary search strategy. The obtained results are also compared with NSGA-II, NSGA-III, MOEA/D, MOEA/D-DE, MOPSO and d-MOPSO state of art multi-objective evolutionary algorithms.

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References

  1. Mirjalili, S., Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016)

    Article  Google Scholar 

  2. Prakash, D.B., Lakshminarayana, C.: Optimal siting of capacitors in radial distribution network using whale optimization algorithm. Alex. Eng. J. 56, 499–509 (2016)

    Article  Google Scholar 

  3. Reddy, P.D.P., Reddy, V.C.V., Manohar, T.G.: Whale optimization algorithm for optimal sizing of renewable resources for loss reduction in distribution systems. Renew. Wind Water Sol. 4(1), 3 (2017)

    Article  Google Scholar 

  4. Mafarja, M., Mirjalili, S.: Whale optimization approaches for wrapper feature selection. Appl. Soft Comput. J. 62(November), 441–453 (2018)

    Article  Google Scholar 

  5. Mostafa, A., Hassanien, A.E., Houseni, M., Hefny, H.: Liver segmentation in MRI images based on whale optimization algorithm. Multimed. Tools Appl. 76(April), 1–24 (2017)

    Google Scholar 

  6. Dao, T.K., Pan, T.S., Pan, J.S.: A multi-objective optimal mobile robot path planning based on whale optimization algorithm. In: 2016 IEEE 13th International Conference on Signal Processing, pp. 337–342 (2016)

    Google Scholar 

  7. Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In: Parallel Problem Solving from Nature PPSN VI, pp. 849–858 (2000)

    Chapter  Google Scholar 

  8. Yagyasen, D., Darbari, M., Shukla, P.K., Singh, V.K.: Diversity and convergence issues in evolutionary multiobjective optimization: application to agriculture science. IERI Procedia 5, 81–86 (2013)

    Article  Google Scholar 

  9. Bosman, P.A.N., Thierens, D.: The balance between proximity and diversity in multi – objective evolutionary algorithms. IEEE Trans. Evol. Comput. 7(2), 174–188 (2003)

    Article  Google Scholar 

  10. Lin, Q., Li, J., Du, Z., Chen, J., Ming, Z.: A novel multi-objective particle swarm optimization with multiple search strategies. Eur. J. Oper. Res. 247(3), 732–744 (2015)

    Article  MathSciNet  Google Scholar 

  11. Sierra, M.R., Coello, C.A.C.: Improving PSO-based multi-objective optimization using crowding, mutation and ∈-dominance. In: International Conference on Evolutionary Multi-criterion Optimization, pp. 505–519. Springer, Heidelberg, March 2005

    Google Scholar 

  12. Deb, K., Agrawal, R.B.: Simulated binary crossover for continuous search space. Complex Syst. 9, 1–34 (1994)

    MathSciNet  MATH  Google Scholar 

  13. Jiang, S., Yang, S.: Convergence versus diversity in multiobjective optimization. In: International Conference on Parallel Problem Solving from Nature, pp. 984–993. Springer, Cham, September 2016

    Chapter  Google Scholar 

  14. Khare, V.: Performance Scaling Multi-objective Evolutionary Algorithms. School of Computer Science, The University of Birmingham, Birmingham (2002)

    Google Scholar 

  15. Ngatchou, P., Zarei, A., El-Sharkawi, A.: Pareto multi objective optimization. In: Proceedings of the 13th International Conference on Intelligent Systems Application to Power Systems 2005, pp. 84–91. IEEE, November 2005

    Google Scholar 

  16. Zhou, A., Jin, Y., Zhang, Q., Sendhoff, B., Tsang, E.: Combining model-based and genetics-based offspring generation for multi-objective optimization using a convergence criterion. In: 2006 IEEE International Conference on Evolutionary Computation, pp. 892–899 (2006)

    Google Scholar 

  17. Tian, Y., Cheng, R., Zhang, X., Jin, Y.: PlatEMO: a MATLAB platform for evolutionary multi-objective optimization. IEEE Comput. Intell. Mag. 12, 73–87 (2017)

    Article  Google Scholar 

  18. Jangir, P., Jangir, N.: Non-dominated sorting whale optimization algorithm (NSWOA): a multi-objective optimization algorithm for solving engineering design problems. Glob. J. Res. Eng.: F Electr. Electron. Eng. 17(4) (2017). Version 1.0

    Google Scholar 

  19. Zitzler, E., Deb, K., Thiele, L.: Comparison of multiobjective evolutionary algorithms: empirical results. Evol. Comput. 8, 173–195 (2000)

    Article  Google Scholar 

  20. Deb, K., Thiele, L., Laumanns, M., Zitzler, E.: Scalable test problems for evolutionary multi-objective optimization. In: Evolutionary Multiobjective Optimization, Advanced Information and Knowledge Processing, pp. 105–145. Springer, London (2005)

    Google Scholar 

  21. Li, H., Zhang, Q., Deng, J.: Biased multiobjective optimization and decomposition algorithm. IEEE Trans. Cybern. 47, 52–66 (2016)

    Article  Google Scholar 

  22. Deb, K., Jain, H.: An evolutionary many- objective optimization algorithm using reference-point-based nondominated sorting approach, part I: solving problems with box constraints. IEEE Trans. Evol. Comput. 18(4), 577–601 (2014)

    Article  Google Scholar 

  23. Kumawat, I.R., Nanda, S.J., Maddila, R.K.: Multi-objective whale optimization. TENCON - IEEE Region 10 Conference, November-2017

    Google Scholar 

  24. Zhang, Q., Li, H.: MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11(6), 712–731 (2007)

    Article  Google Scholar 

  25. Li, H., Zhang, Q.: Comparison between NSGA-II and MOEA/D on a set of multiobjective optimization problems with complicated pareto sets. IEEE Trans. Evol. Comput. 13(2), 284–302 (2009)

    Article  Google Scholar 

  26. Parsopoulos, K., Vrahatis, M.N.: Particle swarm optimization method in multiobjective problems. In: SAC 2002, Madrid, Spain (2002)

    Google Scholar 

  27. Zapotecas Martínez, S., Coello Coello, C.A.: A multi-objective particle swarm optimizer based on decomposition. In: Proceeding of the 13th Annual Conference on Genetic and Evolutionary Computation - GECCO ’11, p. 69 (2011)

    Google Scholar 

  28. Zhang, Q., Zhou, A., Zhao, S., Suganthan, P.N., Liu, W., Tiwari, S.: Multiobjective optimization test instances for the CEC 2009 special session and competition. University of Essex, Colchester, UK and Nanyang Technological University, Technical report. CES-487, Technical report (2008)

    Google Scholar 

  29. Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science 1995. MHS’95, pp. 39–43. IEEE, October 1995

    Google Scholar 

  30. El Aziz, M.A., Ewees, A.A., Hassanien, A.E.: Multi-objective whale optimization algorithm for content-based image retrieval. Multimed. Tools Appl. 77, 1–38 (2018)

    Article  Google Scholar 

  31. Wang, J., Du, P., Niu, T., Yang, W.: A novel hybrid system based on a new proposed algorithm—multi-objective whale optimization algorithm for wind speed forecasting. Appl. Energy 208(October), 344–360 (2017)

    Article  Google Scholar 

  32. Cheng, R., et al.: Benchmark functions for the CEC 2017 competition on evolutionary many-objective optimization (2017)

    Google Scholar 

  33. https://www.mathworks.com/matlabcentral/fileexchange/55667-the-whale-optimization-algorithm

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Author information

Authors and Affiliations

  1. United International University, Dhaka, 1212, Bangladesh

    Faisal Ahmed Siddiqi & Chowdhury Mofizur Rahman

Authors
  1. Faisal Ahmed Siddiqi
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  2. Chowdhury Mofizur Rahman
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Corresponding author

Correspondence to Faisal Ahmed Siddiqi .

Editor information

Editors and Affiliations

  1. Machine Intelligence Research Labs, Auburn, WA, USA

    Ajith Abraham

  2. School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

    Aswani Kumar Cherukuri

  3. Tijuana Institute of Technology, Tijuana, Mexico

    Patricia Melin

  4. Machine Intelligence Research Labs, Auburn, WA, USA

    Niketa Gandhi

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Siddiqi, F.A., Mofizur Rahman, C. (2020). Evolutionary Multi-objective Whale Optimization Algorithm. In: Abraham, A., Cherukuri, A., Melin, P., Gandhi, N. (eds) Intelligent Systems Design and Applications. ISDA 2018 2018. Advances in Intelligent Systems and Computing, vol 941. Springer, Cham. https://doi.org/10.1007/978-3-030-16660-1_43

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