Memetic Search in Differential Evolution Algorithm
Sandeep Kumar, Vivek Kumar Sharma, Rajani Kumari

TL;DR
This paper introduces Memetic Search in Differential Evolution (MSDE), a novel strategy that enhances DE's performance by preventing stagnation and improving convergence speed, validated through benchmark and real-world tests.
Contribution
The paper proposes a new memetic search strategy integrated into DE, inspired by artificial bee colony algorithms, to improve optimization performance.
Findings
MSDE outperforms basic DE on benchmark problems.
MSDE shows better convergence speed.
MSDE effectively avoids stagnation.
Abstract
Differential Evolution (DE) is a renowned optimization stratagem that can easily solve nonlinear and comprehensive problems. DE is a well known and uncomplicated population based probabilistic approach for comprehensive optimization. It has apparently outperformed a number of Evolutionary Algorithms and further search heuristics in the vein of Particle Swarm Optimization at what time of testing over both yardstick and actual world problems. Nevertheless, DE, like other probabilistic optimization algorithms, from time to time exhibits precipitate convergence and stagnates at suboptimal position. In order to stay away from stagnation behavior while maintaining an excellent convergence speed, an innovative search strategy is introduced, named memetic search in DE. In the planned strategy, positions update equation customized as per a memetic search stratagem. In this strategy a better…
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