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

机译：基于归一化和改进变异策略的多目标差分进化

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摘要

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.
机译：在许多应用中，开发解决多目标优化问题的有效算法是一项艰巨而必不可少的任务。此任务涉及需要同时优化的两个或多个冲突目标。许多现实世界中的问题都属于此类。我们介绍了改进的多目标差分进化（DE）算法，即MOnDE，它使用新的变异策略和归一化方法来选择非控制解。新的变异策略“ DE / rand-to-nbest”在所有目标方面均使用最佳标准化个体，以指导寻找真正的最优解决方案。结果，增加了产生优良解的可能性，并且实现了更快的收敛。使用归一化目标值的求和方法来代替非支配排序，以克服对非支配解进行排序的高计算复杂性和开销问题。我们的方法的性能在一组由两到五个目标组成的基准问题上进行了测试。比较使用了多目标进化规划和多目标差分进化算法的不同组合。结果证实了该方法在文献中其他知名算法中的有效性和鲁棒性。

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