An improved differential evolution algorithm with fitness-based adaptation of the control parameters

Ghosh, Arnob; Das, Swagatam; Chowdhury, Aritra; Giri, Ritwik

doi:10.1016/j.ins.2011.03.010

Cited by 142 publications

(53 citation statements)

References 27 publications

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“…These problems are briefly summarized in Table 7. We compare total emigrationbased BBO, which generally provided better performance than the other variations of BBO in the previous sections, with stud GA (which we call SGA) [15], standard PSO 2007 (which we call SPSO 07) [4], [23], and adaptive DE (which we call ADE) [5,11,14,32,38]. We compare with SGA because SGA is an improvement of the classic GA and uses the best individual at each generation for crossover.…”

Section: Real-world Optimization Resultsmentioning

confidence: 99%

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Variations of biogeography-based optimization and Markov analysis

Simon

Fei

et al. 2013

Information Sciences

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Section: Real-world Optimization Resultsmentioning

confidence: 99%

“…Now suppose that the mutation probability is the same as that in partial immigration-based BBO. That is, P ð2Þ ðvÞ is defined ki as in (11), except that we use P ki (v) from (13) instead of P ki (v) from (10). Then the transition matrix for total immigrationbased BBO is calculated as shown in (12).…”

Section: Total Immigration-based Bbomentioning

confidence: 99%

Variations of biogeography-based optimization and Markov analysis

Simon

Fei

et al. 2013

Information Sciences

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“…In other words, the design requires finding the proper lengths of transmission lines d 1 , d 2 , l 1 and l 2 at the central operation frequency. The power function that needs to be optimized is given by the following expression: This expression depends on transistor s-parameters: S 11 , S 12 , S 21 and S 22 . The reflection parameters are Γ S and Γ L for source and load respectively.…”

Section: Microwave Amplifier Designmentioning

confidence: 99%

Core Levels Algorithm for Optimization: Case of Microwave Models

Haydar¹,

Ülker²,

Dimililer³

et al. 2017

ijacsa

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Abstract-Metaheuristic algorithms are investigated and used by many researchers in different areas. It is crucial to find optimal solutions for all problems under study especially for the ones which require sensitive optimization. Especially, for real case problems, solution quality and convergence speed of the algorithms are highly desired characteristics. In this paper, a new optimization algorithm called Core Levels Algorithm (COLA) based on the use of metaheuristics is proposed and analyzed. In the algorithm, two core levels are applied recursively to create new offsprings from the parent vectors which provides a desired balance on the exploration and exploitation characteristics. The algorithm's performance is first studied on some well-known benchmark functions and then compared with previously proposed efficient evolutionary algorithms. The experimental results showed that even at the early stages of optimization, obtained values are very close or exactly the same as the optimum values of the analyzed functions. Then, the performance of COLA is investigated on real case problems such as some selected microwave circuit designs. The results denoted that COLA produces stable results and provides high accuracy of optimization without high parameter dependency even for the real case problems.

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“…In success-history based adaptive DE (SHADE) [31], a new parameter adaptation mechanism which is based on the successful searching experience is proposed. Many variants of parameters control such as FiADE, DMPSADE and DESSA are available in the literature [32][33][34].…”

Section: Adapting Control Parameters Of Differential Evolutionmentioning

confidence: 99%

A Modified Cloud Particles Differential Evolution Algorithm for Real-Parameter Optimization

2016

Algorithms

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Abstract:The issue of exploration-exploitation remains one of the most challenging tasks within the framework of evolutionary algorithms. To effectively balance the exploration and exploitation in the search space, this paper proposes a modified cloud particles differential evolution algorithm (MCPDE) for real-parameter optimization. In contrast to the original Cloud Particles Differential Evolution (CPDE) algorithm, firstly, control parameters adaptation strategies are designed according to the quality of the control parameters. Secondly, the inertia factor is introduced to effectively keep a better balance between exploration and exploitation. Accordingly, this is helpful for maintaining the diversity of the population and discouraging premature convergence. In addition, the opposition mechanism and the orthogonal crossover are used to increase the search ability during the evolutionary process. Finally, CEC2013 contest benchmark functions are selected to verify the feasibility and effectiveness of the proposed algorithm. The experimental results show that the proposed MCPDE is an effective method for global optimization problems.

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An improved differential evolution algorithm with fitness-based adaptation of the control parameters

Cited by 142 publications

References 27 publications

Variations of biogeography-based optimization and Markov analysis

Variations of biogeography-based optimization and Markov analysis

Core Levels Algorithm for Optimization: Case of Microwave Models

A Modified Cloud Particles Differential Evolution Algorithm for Real-Parameter Optimization

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