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Article
Computer Science, Information Systems
Yintong Li et al.
Summary: In this study, an improved differential evolution algorithm named IDE-EDA is proposed by hybridizing the estimation-of-distribution algorithm. The cooperative evolutionary framework combines LSHADE-RSP with EDA to enhance the exploitation capability. A new control parameter is introduced to balance exploitation and exploration, and a greed strategy is used to retain high-quality solutions. Comparison with state-of-the-art variants demonstrates the efficiency of IDE-EDA.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Haotian Zhang et al.
Summary: This paper proposes a unified mutation operator with learnable parameters, which can achieve parameter control and operator selection by adjusting parameters. By using a neural network to adaptively determine control parameters, experimental results show that embedding the learned unified mutation operator can improve the performance of three different differential evolution algorithms.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Yuzhen Li et al.
Summary: This paper proposes a Differential Evolution using Leader-Adjoint populations (LADE) algorithm, which integrates four mutation strategies to meet the needs of exploration and exploitation at different evolutionary stages. The leader population adopts two mutation strategies for exploration, while the adjoint population uses two mutation strategies for exploitation. Through the interaction and collaboration between both populations, LADE achieves a good trade-off between exploration and exploitation.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Dong Liu et al.
Summary: This paper proposes a simple and effective mutation scheme named DE/current-to-rwrand/1 to enhance the optimization ability of differential evolution (DE) in solving complex optimization problems. The proposed mutation strategy, called function value ranking aware differential evolution (FVRADE), balances high diversity and fast convergence of the population. Experimental results demonstrate that FVRADE outperforms several state-of-the-art methods and shows promise in solving real-world optimization problems.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Engineering, Aerospace
Xuzhao Chai et al.
Summary: The path planning of Unmanned Aerial Vehicle (UAV) in a complex environment is a challenging optimization problem with multiple objectives and constraints. In this work, a Multi-Strategy Fusion Differential Evolution algorithm (MSFDE) is proposed, which integrates multiple strategies to design a high-quality planner for UAVs by balancing exploitation and exploration capabilities. Simulation results demonstrate the outstanding performance of MSFDE in three-dimensional path planning for UAVs in complex environments.
AEROSPACE SCIENCE AND TECHNOLOGY
(2022)
Article
Thermodynamics
Dexuan Zou et al.
Summary: The new differential evolution algorithm improves performance in solving the combined heat and power dynamic economic dispatch problem by introducing an attracting factor and a method of repairing solutions.
Article
Computer Science, Artificial Intelligence
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)
Article
Computer Science, Interdisciplinary Applications
Marcia de Fatima Morais et al.
Summary: This paper proposes three optimization algorithms based on discrete differential evolution (DE) metaheuristics for permutation flow shop (PFS) scheduling problems. The performance of the algorithms is evaluated using various benchmarks, and the results show promising and competitive performance in terms of average performance values.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Computer Science, Information Systems
Zhiping Tan et al.
Summary: Differential evolution (DE) is an efficient evolutionary algorithm for solving continuous or discrete numerical optimization problems. This paper proposes a dynamic fitness landscape-based adaptive mutation strategy selection differential evolution (DFLDE) algorithm, which selects the optimal mutation strategy based on the dynamic fitness landscape characteristics of each optimization problem. Experimental results show that DFLDE outperforms five well-known DE variants in terms of searching for the optimal value, convergence speed, and robustness.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Mengmeng Sheng et al.
Summary: This paper proposes an adaptive neighborhood mutation-based memetic differential evolution algorithm for multimodal optimization. The algorithm conducts diverse search in the early stage and intensive search in the later stage of evolution. It encourages promising individuals for exploitation and unpromising individuals for exploration using an adaptive strategy. Additionally, an adaptive Gaussian-based local search strategy is utilized to improve promising individuals during evolution.
Article
Computer Science, Artificial Intelligence
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)
Article
Automation & Control Systems
Gai-Ge Wang et al.
Summary: The job-shop scheduling problem is of great practical significance, but is difficult to solve due to many uncontrollable factors. The introduction of fuzzy processing time and completion time allows for a more comprehensive scheduling model, which can be optimized using a hybrid adaptive differential evolution algorithm. Experimental results show that this algorithm outperforms other state-of-the-art algorithms.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Computer Science, Information Systems
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)
Article
Computer Science, Information Systems
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)
Article
Computer Science, Artificial Intelligence
Wu Deng et al.
Summary: This paper introduces an improved adaptive DE algorithm ACDE/F, which addresses the premature convergence and local optimization issues commonly seen in DE algorithms by incorporating belief space strategy, generalized opposition-based learning strategy, and parameter adaptive strategy. Experimental results demonstrate that ACDE/F outperforms other algorithms in optimization performance and exhibits good performance in practical applications.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Information Systems
Xiaosi Li et al.
Summary: Differential evolution algorithm shows good performance but suffers from local optimal trapping and premature evolution issues. PAIDDE algorithm improves DE by utilizing information feedback, outperforming other state-of-the-art algorithms in solution quality across various benchmark functions and real-world problems.
Article
Computer Science, Artificial Intelligence
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.
Article
Computer Science, Information Systems
Xuewen Xia et al.
Summary: In this paper, a novel differential evolution algorithm, NFDDE, is proposed with a hybrid fitness driving force to balance the trade-off between exploration and exploitation. The algorithm considers both fitness and novelty values of individuals, utilizing adaptive scaling factors to effectively leverage the distinct properties of the two driving forces. Additionally, individuals with lower novelty are deleted when the population converges, saving computational resources.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Information Systems
Zhenyu Meng et al.
Summary: This paper introduces a Cooperative Strategy based Differential Evolution (CS-DE) algorithm with enhanced population diversity, utilizing two similar mutation strategies to tackle complex black-box optimization problems. Experimental results demonstrate the competitiveness of the CS-DE algorithm with several state-of-the-art DE variants on the CEC2013 and CEC2014 test suites.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Information Systems
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)
Article
Computer Science, Information Systems
Boxin Guan et al.
Summary: The paper introduces a fast evolutionary optimization method called search history-guided differential evolution for selecting feature combinations from high-dimensional data. Comparative studies show that the proposed algorithm has superior performance in selecting feature combinations, providing a reference for studying the functional mechanisms of related diseases.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Jianchao Cheng et al.
Summary: DE is a global optimization algorithm that relies on mutation operation and individual positions. This paper introduces a new mutation operator, FDDE, which assigns suitable positions by considering both individuals' fitness and diversity contributions. The experimental results show that FDDE outperforms its competitors in convergence performance on various benchmark sets.
SWARM AND EVOLUTIONARY COMPUTATION
(2021)
Article
Mathematical & Computational Biology
Hadi Bayzidi et al.
Summary: The paper introduces a new metaheuristic optimization algorithm called social network search (SNS), which mimics the decision moods of social network users in expressing opinions. The algorithm shows effectiveness in solving engineering optimization problems by modeling real-world user behaviors in social networks.
COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
(2021)
Article
Engineering, Multidisciplinary
Laith Abualigah et al.
Summary: The Arithmetic Optimization Algorithm (AOA) is a new meta-heuristic method that makes use of the distribution behavior of arithmetic operators, demonstrating promising results in solving challenging optimization problems across various search spaces.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2021)
Article
Computer Science, Information Systems
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.
Article
Computer Science, Artificial Intelligence
Yongjie Ma et al.
APPLIED INTELLIGENCE
(2020)
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Caihua Chen et al.
APPLIED SOFT COMPUTING
(2020)
Article
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Zhenyu Meng et al.
Article
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Ling Gui et al.
SWARM AND EVOLUTIONARY COMPUTATION
(2019)
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Yirui Wang et al.
SWARM AND EVOLUTIONARY COMPUTATION
(2019)
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Radka Polakova et al.
SWARM AND EVOLUTIONARY COMPUTATION
(2019)
Article
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Zhihui Li et al.
SWARM AND EVOLUTIONARY COMPUTATION
(2019)
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Xin Zhang et al.
Article
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Zhenyu Meng et al.
Proceedings Paper
Engineering, Electrical & Electronic
Abhishek Kumar et al.
2019 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC)
(2019)
Article
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Ali Wagdy Mohamed et al.
Article
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Mostafa Z. Ali et al.
IEEE TRANSACTIONS ON CYBERNETICS
(2017)
Article
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Nandar Lynn et al.
APPLIED SOFT COMPUTING
(2017)
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Yiqiao Cai et al.
FRONTIERS OF COMPUTER SCIENCE
(2016)
Article
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Yong Wang et al.
APPLIED SOFT COMPUTING
(2016)
Article
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Wenchao Yi et al.
EXPERT SYSTEMS WITH APPLICATIONS
(2016)
Article
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Zhenyu Meng et al.
KNOWLEDGE-BASED SYSTEMS
(2016)
Article
Automation & Control Systems
Wenyin Gong et al.
IEEE TRANSACTIONS ON CYBERNETICS
(2015)
Article
Automation & Control Systems
Jiahai Wang et al.
IEEE TRANSACTIONS ON CYBERNETICS
(2014)
Article
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Wenyin Gong et al.
IEEE TRANSACTIONS ON CYBERNETICS
(2013)
Article
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Yong Wang et al.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2011)
Article
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Joaquin Derrac et al.
SWARM AND EVOLUTIONARY COMPUTATION
(2011)
Article
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A. K. Qin et al.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2009)
Article
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Jingqiao Zhang et al.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2009)
Article
Computer Science, Artificial Intelligence
Janez Brest et al.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2006)
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J Liu et al.
