One-array Differential Evolution Algorithm with a Novel Replacement Strategy for Numerical Optimization | IEEE Conference Publication | IEEE Xplore

One-array Differential Evolution Algorithm with a Novel Replacement Strategy for Numerical Optimization


Abstract:

Differential Evolution (DE) algorithm is an efficient metaheuristic algorithm in solving complex real-world optimization problems. DE algorithm benefits from two populati...Show More

Abstract:

Differential Evolution (DE) algorithm is an efficient metaheuristic algorithm in solving complex real-world optimization problems. DE algorithm benefits from two populations for updating individuals, while it might cause memory problems in practice during solving large-scale optimization problems; especially when they are used in an embedded system. One strategy to tackle this problem is utilizing a one-array scheme which benefits from only one population, leading to a half-space memory. This paper proposes a novel DE algorithm based on one-array DE and a random replacement strategy; it adds an additional competition to the selection operator to make better use of the new individual that it might be potentially noteworthy. The positive feature of the introduced replacement strategy is that it does not need any extra computational budget. Also, due to employing one-array strategy, the proposed scheme has a lower memory complexity. Our experiments on CEC-2017 benchmark function with dimensions 30, 50, and 100 clearly illustrate the effectiveness of the proposed DE algorithm.
Date of Conference: 11-14 October 2020
Date Added to IEEE Xplore: 14 December 2020
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Conference Location: Toronto, ON, Canada

References

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