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

• Published in: Natural computing 2017-12, Vol.16 (4), p.661-675
• Main Authors: Awad, Noor H., Ali, Mostafa Z., Duwairi, Rehab M.
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Complex Systems; Computer Science; Evolutionary algorithms; Evolutionary Biology; Mathematical programming; Multiple objective analysis; Objectives; Optimization algorithms; Processor Architectures; Strategy; Theory of Computation
• ISSN: 1567-7818; 1572-9796
• DOI: 10.1007/s11047-016-9585-y

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 MO n DE that uses a new mutation strategy and a normalization method to select non-dominated solutions. The new mutation strategy “DE/rand-to- n best” 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.