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Multi-objective orthogonal opposition-based crow search algorithm for large-scale multi-objective optimization

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Abstract

Many engineering optimization problems are typically multi-objective in their natures and multidisciplinary with a large number of decision variables. Furthermore, Pareto dominance loses its effectiveness in such situations. Thus, developing a robust optimization algorithm undoubtedly becomes a true challenge. This paper proposes a multi-objective orthogonal opposition-based crow search algorithm (M2O-CSA) for solving large-scale multi-objective optimization problems (LSMOPs). In the M2O-CSA, a multi-orthogonal opposition strategy is employed to mitigate the conflicts among the convergence and distribution of solutions. First, two individuals are randomly chosen to undergo the crossover stage and then orthogonal array is presented to obtain nine individuals. Then individuals are used in the opposition stage to improve the diversity of solutions. The effectiveness of the proposed M2O-CSA is investigated by implementing it on different dimensions of multi-objective optimization problems (MOPs). The Pareto front solutions of these MOPs have various characteristics such as convex, non-convex and discrete. It is also applied to solve multi-objective design applications with distinctive features such as four bar truss (FBT) design, welded beam (WB) deign, disk brake (DB) design, and speed reduced (SR) design, where they involve different characteristics. In this context, a new decision making tool based on multi-objective optimization on the basis of ratio analysis (MOORA) technique is employed to help the designer for extracting the operating point as the best compromise or satisfactory solution to execute the candidate engineering design. Simulation results affirm that the proposed M2O-CSA works efficiently and effectively.

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Authors and Affiliations

  1. Faculty of Engineering, Menoufia University, Shebin El-Kom, Egypt

    Rizk M. Rizk-Allah

  2. Faculty of Computers and Artificial Intelligence, Cairo University, Cairo, Egypt

    Aboul Ella Hassanien

  3. Department of Electronics and Computer Science, Koszalin University of Technology, Koszalin, Poland

    Adam Slowik

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  1. Rizk M. Rizk-Allah
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  2. Aboul Ella Hassanien
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  3. Adam Slowik
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Correspondence to Aboul Ella Hassanien.

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Rizk M. Rizk-Allah and Aboul Ella Hassanien: Scientific Research Group in Egypt.

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Rizk-Allah, R.M., Hassanien, A.E. & Slowik, A. Multi-objective orthogonal opposition-based crow search algorithm for large-scale multi-objective optimization. Neural Comput & Applic 32, 13715–13746 (2020). https://doi.org/10.1007/s00521-020-04779-w

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