Population Segmentation-Based Variant of Differential Evolution Algorithm

Pooja, .; Chaturvedi, Praveena; Kumar, Pravesh

doi:10.1007/978-981-10-0451-3_37

Cited by 2 publications

(1 citation statement)

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“…MPEDE was proposed with multiple population structure (derived from single base population) along with selected approach as strategy set [22]. DEPT is an approach of DE having population segmentation [23]. MCDE was proposed with multi-population mechanism for three different mutation strategies along with co-variance approach for cross-over operation [24] while Multi-population differential evolution with balanced ensemble of mutation strategies was delivered by Ali et al [25].…”

Section: Literature Reviewmentioning

confidence: 99%

Exploring Mutation Strategy of Evolution Algorithm for Global Optimization Problem

Mishra

Pooja

Ali

2023

Preprint

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Differential Evolution (DE) is a widely used algorithm for solving global optimization problems. The success of DE heavily relies on its mutation operation, which plays a crucial role in generating diverse and high-quality solutions. In this paper different mutation operations for enhancing the performance of DE in global optimization tasks has been considered. Here, a novel mutation strategy that aim to strike a balance between exploration and exploitation to improve the convergence speed and quality has been proposed. The proposed DE is basically focused on novel mutation-based strategy where a new coefficient factor $("\sigma")$ is involved with the base vector in basic mutation strategy $("DE/rand/1")$ to enhance the convergence of local variable during the exploitation and to improve the convergence rate as well as convergence quality. Additionally, we evaluate the proposed mutation operations on a set of 27 benchmark functions commonly used in global optimization. Experimental results demonstrate that the enhanced mutation strategies significantly outperform the state-of-the-art algorithms in terms of solution accuracy, convergence speed. The findings highlight the importance of mutation operations in DE and provide valuable insights into designing more effective mutation strategies for tackling complex global optimization problems.

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