Research on an Improved Differential Evolution Algorithm based on Three Strategies for Solving Complex Function

Hao Jia 보안공학연구지원센터 2015 International Journal of Smart Home Vol.9 No.11

For the shortcomings of differential evolution algorithm(DE), such as the low convergence rate in the late evolution, easy to trap into the local optimal solution, and weak situation of the global search ability and the stability of optimization, an improved differential evolution algorithm based on multi-population and dynamic local search(MPDLSDE) is proposed in this paper. In the MPDLSDE algorithm, different populations select different mutation operation model in order to obtain superiority reciprocity between different models in the process of evolution. And the random selected method and small probability perturbation are used to increase the diversity of population and balance the exploitation ability and exploration ability of the algorithm. Then dynamic local search method is used to solve the current optimal solution in order to speed up the convergence rate. Several well-known benchmark functions are selected to validate the efficiency of the MPDLSDE algorithm. The simulation experiment and comparative analysis results show that the MPDLSDE algorithm can enhance the global convergence ability and get the high accuracy solution in high dimensional complex optimization problems.

개선된 미분 진화 알고리즘에 의한 퍼지 모델의 설계

김현기,오성권 한국지능시스템학회 2012 한국지능시스템학회논문지 Vol.22 No.4

Evolutionary algorithms such as genetic algorithm (GA) have been proven their effectiveness when applying to the design of fuzzy models. However, it tends to suffer from computationally expensWive due to the slow convergence speed. In this study, we propose an approach to develop fuzzy models by means of an improved differential evolution (IDE) to overcome this limitation. The improved differential evolution (IDE) is realized by means of an orthogonal approach and differential evolution. With the invoking orthogonal method, the IDE can search the solution space more efficiently. In the design of fuzzy models, we concern two mechanisms, namely structure identification and parameter estimation. The structure identification is supported by the IDE and C-Means while the parameter estimation is realized via IDE and a standard least square error method. Experimental studies demonstrate that the proposed model leads to improved performance. The proposed model is also contrasted with the quality of some fuzzy models already reported in the literature.

Design of Fuzzy Models with the Aid of an Improved Differential Evolution

Hyun-Ki Kim(김현기),Sung-Kwun Oh(오성권) 한국지능시스템학회 2012 한국지능시스템학회논문지 Vol.22 No.4

Evolutionary algorithms such as genetic algorithm (GA) have been proven their effectiveness when applying to the design of fuzzy models. However, it tends to suffer from computationally expensWive due to the slow convergence speed. In this study, we propose an approach to develop fuzzy models by means of an improved differential evolution (IDE) to overcome this limitation. The improved differential evolution (IDE) is realized by means of an orthogonal approach and differential evolution. With the invoking orthogonal method, the IDE can search the solution space more efficiently. In the design of fuzzy models, we concern two mechanisms, namely structure identification and parameter estimation. The structure identification is supported by the IDE and C-Means while the parameter estimation is realized via IDE and a standard least square error method. Experimental studies demonstrate that the proposed model leads to improved performance. The proposed model is also contrasted with the quality of some fuzzy models already reported in the literature.

인위적 진화의 세계

이재원(Lee, Jae-Won) 동남어문학회 2012 동남어문논집 Vol.1 No.33

This writing is an attempt to examine the logic behind artificial evolution in the early writing of Lee Kwang-su. According to his account, human civilization is the result of artificial evolution. By defining human personality as abilities of artificial evolution, he tried to change the way of basic understanding human being. He presented in the existence which comes true the essence of the human being which is such the civilized person. At logic of his artificial evolution, the problem of civilization was converted with creation of individual all who have the desire toward civilization. He looked at the situation and position of korean society from the viewpoint of world history and admitted the differential temporality which is dividing between korean society and western civilization. The Lee’s ideas of artificial evolution serves as alternative way to jump over the differential temporality. In the early writing, the methods of the artificial evolution are reform of education system, reading literature and revolution. Through this method, Lee tried to prepare ‘Civilized people’ and ‘shared commitment to social values’ that are practical foundation of artificial evolution.

An evolving surrogate model-based differential evolution algorithm

Mallipeddi, R.,Lee, M. Elsevier Science, B.V 2015 Applied soft computing Vol.34 No.-

Differential evolution (DE) is a simple and effective approach for solving numerical optimization problems. However, the performance of DE is sensitive to the choice of mutation and crossover strategies and their associated control parameters. Therefore, to achieve optimal performance, a time-consuming parameter tuning process is required. In DE, the use of different mutation and crossover strategies with different parameter settings can be appropriate during different stages of the evolution. Therefore, to achieve optimal performance using DE, various adaptation, self-adaptation, and ensemble techniques have been proposed. Recently, a classification-assisted DE algorithm was proposed to overcome trial and error parameter tuning and efficiently solve computationally expensive problems. In this paper, we present an evolving surrogate model-based differential evolution (ESMDE) method, wherein a surrogate model constructed based on the population members of the current generation is used to assist the DE algorithm in order to generate competitive offspring using the appropriate parameter setting during different stages of the evolution. As the population evolves over generations, the surrogate model also evolves over the iterations and better represents the basin of search by the DE algorithm. The proposed method employs a simple Kriging model to construct the surrogate. The performance of ESMDE is evaluated on a set of 17 bound-constrained problems. The performance of the proposed algorithm is compared to state-of-the-art self-adaptive DE algorithms: the classification-assisted DE algorithm, regression-assisted DE algorithm, and ranking-assisted DE algorithm.

Adaptive Differential Evolution-based Receding Horizon Control Design for Multi-UAV Formation Reconfiguration

Boyang Zhang,Xiuxia Sun,Shuguang Liu,Xiongfeng Deng 제어·로봇·시스템학회 2019 International Journal of Control, Automation, and Vol.17 No.12

The complicated and constrained reconfiguration optimisation for unmanned aerial vehicles (UAVs) is a challenge, particularly when multi-mission requirements are taken into account. In this study, we evaluate the use of the adaptive differential evolution-based centralised receding horizon control approach to achieve the formation reconfiguration along a given formation group trajectory for multiple unmanned aerial vehicles in a three-dimensional (3D) environment. A rolling optimisation approach which combines the receding horizon control method with the adaptive differential evolution algorithm is proposed, where the receding horizon control method divides the global control problem into a series of local optimisations and each local optimisation problem is solved by an adaptive differential evolution algorithm. Furthermore, a novel quadratic reconfiguration cost function with the topology information of UAVs is presented, and the asymptotic convergence of the rolling optimisation is analysed. Finally, simulation examples are provided to illustrate the validity of the proposed control structure.

Special Protection and Control Scheme for Transmission Line Overloading Elimination Based on Hybrid Differential Evolution/Electromagnetism-Like Algorithm

Hadi, Mahmood Khalid,Othman, Mohammad Lutfi,Wahab, Noor Izzri Abd The Korean Institute of Electrical Engineers 2017 Journal of Electrical Engineering & Technology Vol.12 No.5

In designing System Protection Schemes (SPSs) in power systems, protecting transmission network against extreme undesired conditions becomes a significant challenge in mitigating the transmission line overloading. This paper presents an intelligent Special Protection and Control Scheme (SPCS) using of Differential Evolution with Adaptive Mutation (DEAM) approach to obtain the optimum generation rescheduling to solve the transmission line overloading problem in system contingency conditions. DEAM algorithm employs the attraction-repulsion idea that is applied in the electromagnetism-like algorithm to support the mutation process of the conventional Differential Evolution (DE) algorithm. Different N-1 contingency conditions under base and increase load demand are considered in this paper. Simulation results have been compared with those acquired from Genetic Algorithm (GA) application. Minimum severity index has been considered as the objective function. The final results show that the presented DEAM method offers better performance than GA in terms of faster convergence and less generation fuel cost. IEEE 30-bus test system has been used to prove the effectiveness and robustness of the proposed algorithm.

Special Protection and Control Scheme for Transmission Line Overloading Elimination Based on Hybrid Differential Evolution/Electromagnetism-Like Algorithm

Mahmood Khalid Hadi,Mohammad Lutfi Othman,Noor Izzri Abd Wahab 대한전기학회 2017 Journal of Electrical Engineering & Technology Vol.12 No.5

In designing System Protection Schemes (SPSs) in power systems, protecting transmission network against extreme undesired conditions becomes a significant challenge in mitigating the transmission line overloading. This paper presents an intelligent Special Protection and Control Scheme (SPCS) using of Differential Evolution with Adaptive Mutation (DEAM) approach to obtain the optimum generation rescheduling to solve the transmission line overloading problem in system contingency conditions. DEAM algorithm employs the attraction-repulsion idea that is applied in the electromagnetism-like algorithm to support the mutation process of the conventional Differential Evolution (DE) algorithm. Different N-1 contingency conditions under base and increase load demand are considered in this paper. Simulation results have been compared with those acquired from Genetic Algorithm (GA) application. Minimum severity index has been considered as the objective function. The final results show that the presented DEAM method offers better performance than GA in terms of faster convergence and less generation fuel cost. IEEE 30-bus test system has been used to prove the effectiveness and robustness of the proposed algorithm.

입자군집최적화와 차분진화알고리즘 간의 공진화를 활용한 교섭게임 관찰

이상욱(Sangwook Lee) 한국콘텐츠학회 2014 한국콘텐츠학회논문지 Vol.14 No.11

근래에 게임이론 분야에서 진화계산법을 사용한 교섭게임 분석은 중요한 이슈 중에 하나이다. 본 논문에서는 이질적인 두 인공 에이전트 간의 공진화를 활용하여 교섭게임을 관찰한다. 두 인공 에이전트를 모델링하기 위해 사용된 전략은 진화전략의 종류인 입자군집최적화와 차분진화알고리즘이다. 교섭게임에서 각 전략이 최선의 결과를 얻기 위한 알고리즘 모수들을 조사하고 두 전략의 공진화를 관찰하여 어느 알고리즘이 교섭게임에 더 우수한지 관찰한다. 컴퓨터 시뮬레이션 실험 결과 입자군집최적화 전략이 차분진화알고리즘 전략보다 교섭게임에서 더 우수한 성능을 보임을 확인하였다. Recently, analysis of bargaining game using evolutionary computation is essential issues in field of game theory. In this paper, we observe a bargaining game using co-evolution between two heterogenous artificial agents. In oder to model two artificial agents, we use a particle swarm optimization and a differential evolution. We investigate algorithm parameters for the best performance and observe that which strategy is better in the bargaining game under the co-evolution between two heterogenous artificial agents. Experimental simulation results show that particle swarm optimization outperforms differential evolution in the bargaining game.

Differential evolution with multi-population based ensemble of mutation strategies

Wu, G.,Mallipeddi, R.,Suganthan, P.N.,Wang, R.,Chen, H. North-Holland [etc ; Elsevier Science Ltd 2016 Information sciences Vol.329 No.-

<P>Differential evolution (DE) is among the most efficient evolutionary algorithms (EAs) for global optimization and now widely applied to solve diverse real-world applications. As the most appropriate configuration of DE to efficiently solve different optimization problems can be significantly different, an appropriate combination of multiple strategies into one DE variant attracts increasing attention recently. In this study, we propose a multi-population based approach to realize an ensemble of multiple strategies, thereby resulting in a new DE variant named multi-population ensemble DE (MPEDE) which simultaneously consists of three mutation strategies, i.e., 'current-to-pbest/1' and 'current-to-rand/1' and 'rand/1'. There are three equally sized smaller indicator subpopulations and one much larger reward subpopulation. Each constituent mutation strategy has one indicator subpopulation. After every certain number of generations, the current best performing mutation strategy will be determined according to the ratios between fitness improvements and consumed function evaluations. Then the reward subpopulation will be allocated to the determined best performing mutation strategy dynamically. As a result, better mutation strategies obtain more computational resources in an adaptive manner during the evolution. The control parameters of each mutation strategy are adapted independently as well. Extensive experiments on the suit of CEC 2005 benchmark functions and comprehensive comparisons with several other efficient DE variants show the competitive performance of the proposed MPEDE (Matlab codes of MPEDE are available from http://guohuawunudt.gotoip2.com/publications.html). (C) 2015 Elsevier Inc. All rights reserved.</P>