☆ 4.7 Article

Enhancing differential evolution algorithm using leader-adjoint populations

INFORMATION SCIENCES (2023)

相关参考文献

注意：仅列出部分参考文献，下载原文获取全部文献信息。

Article Computer Science, Artificial Intelligence

Population reduction with individual similarity for differential evolution

Yuzhen Li et al.

Summary: This paper proposes an individual similarity population reduction strategy and improves the DE algorithm using an elite-oriented strategy. Experimental results show that this method effectively enhances the performance of the DE algorithm.

ARTIFICIAL INTELLIGENCE REVIEW (2023)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Differential evolution with variable leader-adjoint populations

Yuzhen Li et al.

Summary: This paper proposes a differential evolution algorithm called LADE, which divides the population into leader and adjoint populations using a leader-adjoint model to balance exploration and exploitation abilities at different stages. Experimental results show that LADE outperforms recent and advanced algorithms on CEC2014 benchmark functions and Lennard-Jones potential real-world problem, and the effect of control parameters is evaluated and analyzed.

APPLIED INTELLIGENCE (2023)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Self-adaptive resources allocation-based differential evolution for constrained evolutionary optimization

Kangjia Qiao et al.

Summary: The paper introduces a self-adaptive resources allocation-based differential evolution (SRADE) to balance diversity, convergence, constraints, and objective function in addressing constrained optimization problems. By dynamically assigning different mutation strategies to individuals based on their performance feedback, the method effectively improves search efficiency under limited resources by focusing on the most efficient strategy.

KNOWLEDGE-BASED SYSTEMS (2022)

添加到收藏夹

Article Computer Science, Artificial Intelligence

A backtracking differential evolution with multi-mutation strategies autonomy and collaboration

Yuzhen Li et al.

Summary: This paper introduces a backtracking differential evolution algorithm that uses multi-mutation strategies autonomy and collaboration to solve optimization problems. The experiments show that this algorithm exhibits competitive performance on real test functions and demonstrates the effectiveness and superiority of the evolution backtracking strategy.

APPLIED INTELLIGENCE (2022)

添加到收藏夹

Article Computer Science, Information Systems

Ship-unloading scheduling optimization with differential evolution

Zhen Gao et al.

Summary: This study investigates the scheduling problem for unloading material ships in a large-scale steel plant. It proposes a differential evolution algorithm and a mathematical programming model to optimize the problem. The empirical study demonstrates the potential of these methods for real-world applications.

INFORMATION SCIENCES (2022)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Adaptive differential evolution with ensembling operators for continuous optimization problems

Wenchao Yi et al.

Summary: The paper introduces a new algorithm called adaptive differential evolution with ensembling populations, which balances the exploitation and exploration abilities of the algorithm by utilizing different mutation and crossover operators. It also improves the search efficiency through an adaptive parameter control strategy.

SWARM AND EVOLUTIONARY COMPUTATION (2022)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Cooperative co-evolutionary differential evolution algorithm applied for parameters identification of lithium-ion batteries

Chuan Wang et al.

Summary: This study introduces a new cooperative co-evolution algorithm to identify parameters of lithium-ion batteries, avoiding linearization or pre-assumption, and demonstrates its effectiveness through comprehensive experimental results.

EXPERT SYSTEMS WITH APPLICATIONS (2022)

添加到收藏夹

Article Computer Science, Information Systems

Parameter and strategy adaptive differential evolution algorithm based on accompanying evolution

Minghao Wang et al.

Summary: This paper proposes a parameter and strategy adaptive differential evolution algorithm based on accompanying evolution (APSDE). By optimizing the accompanying population, the strategy and parameters of the main population are adapted, and population diversity is enhanced by generating reverse individuals.

INFORMATION SCIENCES (2022)

添加到收藏夹

Article Computer Science, Information Systems

Improved differential evolution algorithm based on the sawtooth-linear population size adaptive method

Zhiqiang Zeng et al.

Summary: This study proposes an improved differential evolution algorithm called SLDE, which utilizes a sawtooth-linear population size adaptive (SLPSA) method and an improved parameter control method. Experimental results demonstrate that SLDE outperforms six state-of-the-art differential evolution algorithms.

INFORMATION SCIENCES (2022)

添加到收藏夹

Article Computer Science, Information Systems

An adaptive clonal selection algorithm with multiple differential evolution strategies

Yi Wang et al.

Summary: This study develops an improved adaptive clonal selection algorithm with multiple differential evolution strategies. The algorithm introduces an adaptive mutation strategy pool, an adaptive population resizing method, and detection methods for premature convergence and stagnation. Experimental results demonstrate that the proposed method outperforms state-of-the-art clonal selection algorithms and differential evolution algorithms.

INFORMATION SCIENCES (2022)

添加到收藏夹

Article Computer Science, Information Systems

Solving multimodal optimization problems using adaptive differential evolution with archive

Suchitra Agrawal et al.

Summary: This paper proposes an adaptive differential evolution algorithm to solve multimodal optimization problems. The algorithm utilizes a distributed framework, mutation strategy, and elite archive mechanism to locate multiple optimal solutions and improve their accuracy.

INFORMATION SCIENCES (2022)

添加到收藏夹

Article Computer Science, Information Systems

Novel binary differential evolution algorithm for knapsack problems

Ismail M. Ali et al.

Summary: A novel technique is proposed in this paper to make a simple differential evolution algorithm effective for solving binary-based problems, introducing new components and fitness evaluation approach. Experimental results demonstrate the superiority of this new algorithm in terms of solution quality and computational times compared to other state-of-the-art algorithms.

INFORMATION SCIENCES (2021)

添加到收藏夹

Article Computer Science, Information Systems

A face recognition framework based on a pool of techniques and differential evolution

Guilherme Felippe Plichoski et al.

Summary: This study proposes a face recognition framework using the Differential Evolution algorithm as an optimizer and a pool of preprocessing and feature extraction techniques. Experimental results show that the framework is competitive in addressing challenges such as illumination, pose, expression, and occlusion variations.

INFORMATION SCIENCES (2021)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Self-adaptive differential evolution applied to combustion engine calibration

Jose Marcio Fachin et al.

Summary: A new population-based stochastic optimization algorithm HSADE is proposed in this paper, addressing unconstrained global optimization problems by exploring and combining best features of DE algorithms. HSADE achieved optimal performance in benchmark functions testing and automotive sector applications, significantly improving calibration efficiency.

SOFT COMPUTING (2021)

添加到收藏夹

Article Computer Science, Information Systems

A fitness-based adaptive differential evolution algorithm

Xuewen Xia et al.

Summary: The paper introduces a fitness-based adaptive differential evolution algorithm (FADE) that splits the population into multiple small-sized swarms and uses an archive of breeding strategies, allowing individuals within the same swarm to adaptively select their own strategy based on fitness. By adaptively adjusting population size and allocating computational resources based on performance, FADE can effectively address diverse fitness landscapes and achieve distinct search behaviors within each swarm. The effectiveness and efficiency of the newly introduced adaptive strategies are confirmed through comprehensive evaluations and comparisons with other state-of-art DE variants.

INFORMATION SCIENCES (2021)

添加到收藏夹

Article Computer Science, Information Systems

A novel wrapper-based feature subset selection method using modified binary differential evolution algorithm

Omid Tarkhaneh et al.

Summary: This paper proposes a Modified Differential Evolution approach to Feature Selection by utilizing two new mutation strategies, striking a balance between exploration and exploitation to maintain classification performance while reducing the number of features. Comparative experiments show the superiority of the proposed approach on standard datasets.

INFORMATION SCIENCES (2021)

添加到收藏夹

Article Computer Science, Information Systems

Differential Evolution Mutations: Taxonomy, Comparison and Convergence Analysis

Ali Wagdy Mohamed et al.

Summary: Over the past two decades, Differential Evolution (DE) has been proven to be one of the most popular and successful evolutionary algorithms for solving global optimization problems over continuous space. This paper provides a comprehensive analysis of basic and novel mutation strategies proposed between 1995 and 2020, presenting a new taxonomy based on the structure of novel mutations. Through numerical experiments and discussion of theoretical and empirical convergence behavior, the paper offers insights and recommendations for designing and developing effective and efficient DE algorithms.

IEEE ACCESS (2021)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Differential evolution algorithm with elite archive and mutation strategies collaboration

Yuzhen Li et al.

ARTIFICIAL INTELLIGENCE REVIEW (2020)

添加到收藏夹

Article Computer Science, Information Systems

Insights into the effects of control parameters and mutation strategy on self-adaptive ensemble-based differential evolution

Shir Li Wang et al.

INFORMATION SCIENCES (2020)

添加到收藏夹

Article Computer Science, Artificial Intelligence

DSM-DE: a differential evolution with dynamic speciation-based mutation for single-objective optimization

Libao Deng et al.

MEMETIC COMPUTING (2020)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Gaining-sharing knowledge based algorithm for solving optimization problems: a novel nature-inspired algorithm

Ali Wagdy Mohamed et al.

INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS (2020)

添加到收藏夹

Review Computer Science, Artificial Intelligence

Recent trends in the use of statistical tests for comparing swarm and evolutionary computing algorithms: Practical guidelines and a critical review

J. Carrasco et al.

SWARM AND EVOLUTIONARY COMPUTATION (2020)

添加到收藏夹

Article Computer Science, Artificial Intelligence

An improved differential evolution algorithm with dual mutation strategies collaboration

Yuzhen Li et al.

EXPERT SYSTEMS WITH APPLICATIONS (2020)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Adaptive guided differential evolution algorithm with novel mutation for numerical optimization

Ali Wagdy Mohamed et al.

INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS (2019)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Self-adaptive mutation differential evolution algorithm based on particle swarm optimization

Shihao Wang et al.

APPLIED SOFT COMPUTING (2019)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Novel mutation strategy for enhancing SHADE and LSHADE algorithms for global numerical optimization

Ali W. Mohamed et al.

SWARM AND EVOLUTIONARY COMPUTATION (2019)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Enhanced Directed Differential Evolution Algorithm for Solving Constrained Engineering Optimization Problems

Ali Wagdy Mohamed et al.

INTERNATIONAL JOURNAL OF APPLIED METAHEURISTIC COMPUTING (2019)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Real-parameter unconstrained optimization based on enhanced fitness-adaptive differential evolution algorithm with novel mutation

Ali Wagdy Mohamed et al.

SOFT COMPUTING (2018)

添加到收藏夹

Article Computer Science, Information Systems

Adaptive multiple-elites-guided composite differential evolution algorithm with a shift mechanism

Laizhong Cui et al.

INFORMATION SCIENCES (2018)

添加到收藏夹

Article Computer Science, Information Systems

Ensemble of differential evolution variants

Guohua Wu et al.

INFORMATION SCIENCES (2018)

添加到收藏夹

Article Computer Science, Information Systems

Differential evolution with multi-population based ensemble of mutation strategies

Guohua Wu et al.

INFORMATION SCIENCES (2016)

添加到收藏夹

Article Multidisciplinary Sciences

An alternative differential evolution algorithm for global optimization

Ali W. Mohamed et al.

JOURNAL OF ADVANCED RESEARCH (2012)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization

A. K. Qin et al.

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (2009)

添加到收藏夹

Article Computer Science, Artificial Intelligence

JADE: Adaptive Differential Evolution With Optional External Archive

Jingqiao Zhang et al.

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (2009)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems

Janez Brest et al.

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (2006)

添加到收藏夹

© Peeref 2019-2024. All rights reserved.