An Improved Differential Evolution Algorithm Based on Dual-Strategy

Zhong, Xuxu; Cheng, Peng

doi:10.1155/2020/9767282

Cited by 10 publications

(4 citation statements)

References 46 publications

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“…This algorithm demonstrates the procedures of proposed strategies of the In the present study, 27 benchmark functions [26] have been used…”

Section: The Corresponding Proposed Algorithmmentioning

confidence: 99%

“…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]. DSIDE has been introduced with dual strategy based approach where enhanced mutation strategy along with scaling based self-control parameter has been applied [26].…”

Section: Literature Reviewmentioning

confidence: 99%

“…and DISDE [26] with some mathematical expression (+, −, ∽). With these expressions, it became very easy to extract the efficiency of the algorithm in comparison to other variants taken under consideration.…”

Section: Experimental Set-up and Parameter Settingsmentioning

confidence: 99%

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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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“…This algorithm demonstrates the procedures of proposed strategies of the In the present study, 27 benchmark functions [26] have been used…”

Section: The Corresponding Proposed Algorithmmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Exploring Mutation Strategy of Evolution Algorithm for Global Optimization Problem

Mishra

Pooja

Ali

2023

Preprint

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“…Population size NP, scaling factor F, and crossover CR are three key control parameters. In many works in the literature, there is evidence that the performance of DE can be improved by changing these control parameters [19].…”

Section: Introductionmentioning

confidence: 99%

Designing Techno-Economic Off-Grid Photovoltaic System Using an Improved Differential Evolution Algorithm

Rockson,

Md Rasid,

Anuar

et al. 2023

Jurnal Teknologi

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Conventional power generation is one of the main contributors to the phenomenon of the greenhouse effect. This has led to a diversification of electricity sources including environmentally friendly energy sources such as solar energy. Off-grid PV systems have gained some traction due to their cost-effectiveness for rural communities. However, the intermittent nature of solar is the main challenge to developing the off-grid PV system. Moreover, the high capital cost of PV systems as well as the storage batteries becomes the main concern for all PV users. Thus, this study aims to optimize the size of the PV system and battery simultaneously and design a cost-effective off-grid photovoltaic system considering various aspects such as battery power, solar irradiance, and PV panel selection while ensuring system reliability. The proposed system was optimized using improved Differential Evolution (DE) and its effectiveness was tested by comparing the results with Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The Improved DE algorithm provides the highest average cost savings compared to other algorithms, which is $500 per year. It is recommended that this method is very useful in the optimization of off-grid PV systems, considering other uncertainties that affect PV system performance.

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Advanced Selection Operation for Differential Evolution Algorithm

Kumar

Garg²

2022

Design and Applications of Nature Inspired Optimization

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An Improved Differential Evolution Algorithm Based on Dual-Strategy

Cited by 10 publications

References 46 publications

Exploring Mutation Strategy of Evolution Algorithm for Global Optimization Problem

Exploring Mutation Strategy of Evolution Algorithm for Global Optimization Problem

Designing Techno-Economic Off-Grid Photovoltaic System Using an Improved Differential Evolution Algorithm

Advanced Selection Operation for Differential Evolution Algorithm

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