Improved Differential Evolution Algorithm Guided by Best and Worst Positions Exploration Dynamics
Source abstract
Abstract
The exploration of premium and new locations is regarded as a fundamental function of every evolutionary algorithm. This is achieved using the crossover and mutation stages of the differential evolution (DE) method. A best-and-worst position-guided novel exploration approach for the DE algorithm is provided in this study. The proposed version, known as “Improved DE with Best and Worst positions (IDEBW)”, offers a more advantageous alternative for exploring new locations, either proceeding directly towards the best location or evacuating the worst location. The performance of the proposed IDEBW is investigated and compared with other DE variants and meta-heuristics algorithms based on 42 benchmark functions, including 13 classical and 29 non-traditional IEEE CEC-2017 test functions and 3 real-life applications of the IEEE CEC-2011 test suite. The results prove that the proposed approach successfully completes its task and makes the DE algorithm more efficient.
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Research context
OpenAlex topics: Metaheuristic Optimization Algorithms Research · Evolutionary Algorithms and Applications · Advanced Multi-Objective Optimization Algorithms
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
ASH (Mathematics) Department, REC Bijnor, Chandpur 246725, UP, India
Department of Basic Sciences, Preparatory Year, King Faisal University, Al Ahsa 31982, Saudi Arabia
Funding
King Faisal University, Saudi Arabia
References (68)
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