A novel hybrid differential evolution algorithm with modified CoDE and JADE

Li, Genghui; Lin, Qiuzhen; Du, Zhihua; Liang, Zhengping; Lu, Nan; Ming, Zhong

doi:10.1016/j.asoc.2016.06.011

Cited by 66 publications

(20 citation statements)

References 53 publications

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“…Step 4: each parent in each subpopulation produces offspring by using one of the four mutation strategies, DE/rand/1, DE/current-to-best/2, DE/rand/2, and DE/current-to-rand/1 (for details of these strategies, please see [10]) and crossover given in Equation (4). For first 20 generations, probabilities are fixed and set to p 1 = p 2 = p 3 = p 4 = 0.25.…”

Section: Sade-echtmentioning

confidence: 99%

“…Differential evolution (DE) [1] has proven to be a simple and efficient EA for many optimization problems. A number of variants of DE were developed and are in practice for unconstrained/constrained optimization [2][3][4][5][6][7][8][9]. In DE, a random initial population of size NP is generated in the whole search space to a possible extent and the fittest/best with minimum function value in the initial population is found.…”

Section: Introductionmentioning

confidence: 99%

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On the Efficacy of Ensemble of Constraint Handling Techniques in Self-Adaptive Differential Evolution

et al. 2019

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Self-adaptive variants of evolutionary algorithms (EAs) tune their parameters on the go by learning from the search history. Adaptive differential evolution with optional external archive (JADE) and self-adaptive differential evolution (SaDE) are two well-known self-adaptive versions of differential evolution (DE). They are both unconstrained search and optimization algorithms. However, if some constraint handling techniques (CHTs) are incorporated in their frameworks, then they can be used to solve constrained optimization problems (COPs). In an early work, an ensemble of constraint handling techniques (ECHT) is probabilistically hybridized with the basic version of DE. The ECHT consists of four different CHTs: superiority of feasible solutions, self-adaptive penalty, ε -constraint handling technique and stochastic ranking. This paper employs ECHT in the selection schemes, where offspring competes with their parents for survival to the next generation, of JADE and SaDE. As a result, JADE-ECHT and SaDE-ECHT are developed, which are the constrained variants of JADE and SaDE. Both algorithms are tested on 24 COPs and the experimental results are collected and compared according to algorithms’ evaluation criteria of CEC’06. Their comparison, in terms of feasibility rate (FR) and success rate (SR), shows that SaDE-ECHT surpasses JADE-ECHT in terms of FR, while JADE-ECHT outperforms SaDE-ECHT in terms of SR.

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Section: Sade-echtmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On the Efficacy of Ensemble of Constraint Handling Techniques in Self-Adaptive Differential Evolution

et al. 2019

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“…(2) MLCC introduces a novel multi-layer structure, which is in nature different from AMALGAM-SO [37], PAP [25], HDE [21] and MPEDE [44] which only uses one layer. With the multi-layer structure, each individual in MLCC can store, utilize and update its evolution information in multiple layers during the evolution, for example, they can evolve multiple layer-associated adaptive/self-adaptive F and CR parameters.…”

Section: The Mlcc Frameworkmentioning

confidence: 99%

“…This is accomplished by the individual preference-based layer selecting (IPLS) mechanism, that allows each individual to connect to its favorite layer. IPLS differs from existing methods [21,25,28,37,[42][43][44] in three aspects: i) each layer in MLCC has access to the entire population. Although some individuals (i.e.…”

mentioning

confidence: 99%

Multi-layer competitive-cooperative framework for performance enhancement of differential evolution

Zhang

Zheng

Tang

et al. 2019

Information Sciences

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Differential Evolution (DE) is recognized as one of the most powerful optimizers in the evolutionary algorithm (EA) family. Many DE variants were proposed in recent years, but significant differences in performances between them are hardly observed. Therefore, this paper suggests a multi-layer competitive-cooperative (MLCC) framework to facilitate the competition and cooperation of multiple DEs, which in turns, achieve a significant performance improvement. Unlike other multi-method strategies which adopt a multi-population based structure, with individuals only evolving in their corresponding subpopulations, MLCC implements a parallel structure with the entire population simultaneously monitored by multiple DEs assigned to their corresponding layers. An individual can store, utilize and update its evolution information in different layers based on an individual preference based layer selecting (IPLS) mechanism and a computational resource allocation bias (RAB) mechanism. In IPLS, individuals connect to only one favorite layer. While in RAB, high-quality solutions are evolved by considering all the layers. Thus DEs associated in the layers work in a competitive and cooperative manner. The proposed MLCC framework has been implemented on several highly competitive DEs. Experimental studies show that the MLCC variants significantly outperform the baseline DEs as well as several state-of-the-art and up-to-date DEs on CEC benchmark functions.

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“…A good alternative is to use metaheuristic optimization algorithms, such as particle swarm optimization (PSO) and cuckoo search (CS). These metaheuristic optimizers are gradient-free optimizers, which do not require any prior knowledge or rigorous mathematical properties, such as continuity and smoothness (Yang et al, 2018;Li et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Influence of initialization on the performance of metaheuristic optimizers

Liu

Yang

2020

Applied Soft Computing

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All metaheuristic optimization algorithms require some initialization, and the initialization for such optimizers is usually carried out randomly. However, initialization can have some significant influence on the performance of such algorithms. This paper presents a systematic comparison of 22 different initialization methods on the convergence and accuracy of five optimizers: differential evolution (DE), particle swarm optimization (PSO), cuckoo search (CS), artificial bee colony (ABC) algorithm and genetic algorithm (GA). We have used 19 different test functions with different properties and modalities to compare the possible effects of initialization, population sizes and the numbers of iterations. Rigorous statistical ranking tests indicate that 43.37% of the functions using the DE algorithm show significant differences for different initialization methods, while 73.68% of the functions using both PSO and CS algorithms are significantly affected by different initialization methods. The simulations show that DE is less sensitive to initialization, while both PSO and CS are more sensitive to initialization. In addition, under the condition of the same maximum number of function evaluations (FEs), the population size can also have a strong effect. Particle swarm optimization usually requires a larger population, while the cuckoo search needs only a small population size. Differential evolution depends more heavily on the number of iterations, a relatively small population with more iterations can lead to better results. Furthermore, ABC is more sensitive to initialization, while such initialization has little effect on GA. Some probability distributions such as the beta distribution, exponential distribution and Rayleigh distribution can usually lead to better performance. The implications of this study and further research topics are also discussed in detail.

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A novel hybrid differential evolution algorithm with modified CoDE and JADE

Cited by 66 publications

References 53 publications

On the Efficacy of Ensemble of Constraint Handling Techniques in Self-Adaptive Differential Evolution

On the Efficacy of Ensemble of Constraint Handling Techniques in Self-Adaptive Differential Evolution

Multi-layer competitive-cooperative framework for performance enhancement of differential evolution

Influence of initialization on the performance of metaheuristic optimizers

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