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Multi-objective differential evolution based on normalization and improved mutation strategy

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Abstract

Developing efficient algorithms for solving multi-objective optimization problems is a challenging and essential task in many applications. This task involves two or more conflicting objectives that need to be simultaneously optimized. Many real-world problems fall into this category. We introduce an improved version of multi-objective differential evolution (DE) algorithm, namely MOnDE that uses a new mutation strategy and a normalization method to select non-dominated solutions. The new mutation strategy “DE/rand-to-nbest” uses the best normalized individual in terms of all the objectives to guide the search towards the true pareto optimal solutions. As a result, the probability of producing superior solutions is increased and a faster convergence is achieved. Summation of normalized objective values method is used instead of non-domination sorting to overcome the high computational complexity and overhead problems of sorting non-dominated solutions. The performance of our approach is tested on a set of benchmark problems that consist of two to five objectives. Different combinations of multi-objective evolutionary programming and multi-objective differential evolution algorithms have been used for comparisons. The results affirm the efficiency and robustness of the proposed approach among other well-known algorithms from the literature.

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Authors and Affiliations

  1. Electrical and Electronic Engineering, School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, 639798, Singapore

    Noor H. Awad

  2. Computer Information Systems Department, School of Computer Information Systems, Jordan University of Science and Technology, Irbid, 22110, Jordan

    Mostafa Z. Ali & Rehab M. Duwairi

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  1. Noor H. Awad
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  2. Mostafa Z. Ali
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  3. Rehab M. Duwairi
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Correspondence to Noor H. Awad.

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Awad, N.H., Ali, M.Z. & Duwairi, R.M. Multi-objective differential evolution based on normalization and improved mutation strategy. Nat Comput 16, 661–675 (2017). https://doi.org/10.1007/s11047-016-9585-y

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