Differential evolution with guiding archive for global numerical optimization

Zhou, Yue; Wang, Jiahai; Zhou, Yuejiao; Qiu, Zhanyan; Bi, Zhisheng; Cai, Yiqiao

doi:10.1016/j.asoc.2016.02.011

Cited by 27 publications

(13 citation statements)

References 36 publications

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“…where represents the gamma function. According to the relevant literature, the optimal value range for the parameter β is [1,2]. To obtain the best value of β, we executed a sensitivity test on β.…”

Section: The Proposed Calfaso Algorithmmentioning

confidence: 99%

“…Over the past 30 years, metaheuristic algorithms have received extensive attention and rapid development due to their simplicity, efficiency and cleverness [1]. Different sources of inspiration, optimization mechanisms and convergence performance contribute to the design of different versions of metaheuristic algorithms for solving real-world problems [2]- [4]. From the perspective of population, metaheuristic algorithms are roughly divided into two categories: (1) single solution-based algorithms such as simulated annealing (SA) [5], which starts from an initial solution and performs optimization in the search space by simulating the annealing process of solid matter in physics; and (2) population-based metaheuristics, where currently popular algorithms include particle swarm optimization (PSO) [6]- [8], differential evolution (DE) [9], the genetic algorithm (GA) [10], the grey wolf optimizer (GWO) [11], and the gravitational search algorithm (GSA) [12].…”

Section: Introductionmentioning

confidence: 99%

“…The Lévy index β for controlling stability is a key parameter, and changes in the parameter β will affect the distribution of the LF. According to related research [28], [29], when β ∈ [1,2], the LF will obtain the best performance. Therefore, we performed constant and random number sensitivity tests for β to ensure the stability of the algorithm.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Improved Atom Search Optimization With Cellular Automata, a Lévy Flight and an Adaptive Weight Strategy

et al. 2020

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Atom search optimization (ASO) is a newly developed metaheuristic algorithm inspired by molecular basis dynamics. The paramount challenge in ASO is that it is prone to stagnation in local optima and premature convergence. To solve these issues, this paper uses a cellular automata structure, a Lévy flight and adaptive weight strategies to help ASO balance exploration and exploitation; the proposed method is named CALFASO. First, the cellular automata structure provides more information exchange for the population and enriches the diversity of the population. Second, a Lévy flight has the characteristics of a random walk, its short-step long-term search can improve the global search capability of the algorithm, and its short-term long-step mutations can help the algorithm jump out of local optima. Third, adaptive weights can effectively improve convergence performance. Finally, the proposed CALFASO was evaluated on the CEC2017 test set and real-world engineering problems and compared with classical and novel evolutionary algorithms to verify its performance. The experimental results and statistical analysis show that the performance of the proposed CALFASO algorithm is better than that of the other selected algorithms.

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Section: The Proposed Calfaso Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Improved Atom Search Optimization With Cellular Automata, a Lévy Flight and an Adaptive Weight Strategy

et al. 2020

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“…Recently, researchers have used evolutionary algorithms for solving the parameter optimization problem, such as particle swarm optimization (PSO) [12], ant colony optimization (ACO) [13], differential evolution (DE) [14], the artificial bee colony (ABC) algorithm [15], and bacterial foraging optimization (BFO) [16]. In recent years, DE has been widely used in various fields [17,18] and has the advantages of a simple structure, reduced parameter setting requirements, and superior problem solving ability. However, the traditional DE method has a disadvantage, in that it can easily become trapped in a local optimal solution.…”

Section: Introductionmentioning

confidence: 99%

Cooperative Carrying Control for Multi-Evolutionary Mobile Robots in Unknown Environments

2019

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This study provides an effective cooperative carrying and navigation control method for mobile robots in an unknown environment. The manager mode switches between two behavioral control modes—wall-following mode (WFM) and toward-goal mode (TGM)—based on the relationship between the mobile robot and the unknown environment. An interval type-2 fuzzy neural controller (IT2FNC) based on a dynamic group differential evolution (DGDE) is proposed to realize the carrying control and WFM control for mobile robots. The proposed DGDE uses a hybrid method that involves a group concept and an improved differential evolution to overcome the drawbacks of the traditional differential evolution algorithm. A reinforcement learning strategy was adopted to develop an adaptive WFM control and achieve cooperative carrying control for mobile robots. The experimental results demonstrated that the proposed DGDE is superior to other algorithms at using WFM control. Moreover, the experimental results demonstrate that the proposed method can complete the task of cooperative carrying, and can realize navigation control to enable the robot to reach the target location.

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“…In contrast, an increase in CR deteriorates the quality of optimal solutions while a decrease causes the algorithm to be stagnant. The importance F and CR in affecting the convergence velocity and robustness of the search process is tested by preserving a fixed population size and scale factor to prevent premature convergence and stagnation [9].…”

Section: Literature Reviewmentioning

confidence: 99%

Self-adaptive Ensemble Based Differential Evolution

Wang¹,

Ng²,

Morsidi³

2018

IJMLC

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Abstract-Differential evolution (DE) is among the more prominent branch of evolutionary algorithm (EA) innovated for multiple optimization properties. It has been improvised in various practical solutions, whether it is for benchmark testing or real world situations. As compared with other stochastic optimization algorithms such as nature inspired algorithms and evolutionary ones, DE possesses savvy traits in terms of exploration and exploitation within its own domain. With its motives of locating optimal points and minimized solution steps for objective functions, DE relied heavily on the necessity to specify parameter settings that is catered for achieving appropriate convergence values. The exhibited parameter value is seen directly correlated with the quality of the solutions for the underlying optimization problem. However, selection of appropriate parameter values occasionally necessitate for a priori experience and problem dependent on user. In most cases, users emphasize more on solving the optimization problem rather than solving the algorithm itself. Besides that, research work related to parameter study in DE lacks of proper and clear guidance to users. Therefore, there is a need to develop a DE which can adaptively determine the appropriate parameters to solve different optimization problems with minimum guidance from users. In this research, we take the opportunity to develop a DE model which combines self-adaptive and ensemble mechanisms to dynamically change the control parameters as well as mutation strategy during evolution with minimum intervention from users. The experimental results have shown that the proposed model is able to perform adequately well in twenty different benchmark problems without depending on user to determine the parameters explicitly.

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Differential evolution with guiding archive for global numerical optimization

Cited by 27 publications

References 36 publications

An Improved Atom Search Optimization With Cellular Automata, a Lévy Flight and an Adaptive Weight Strategy

An Improved Atom Search Optimization With Cellular Automata, a Lévy Flight and an Adaptive Weight Strategy

Cooperative Carrying Control for Multi-Evolutionary Mobile Robots in Unknown Environments

Self-adaptive Ensemble Based Differential Evolution

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