• 대한기계학회논문집A
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• 제27권11호
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• pp.1809-1814
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• 2003

Differential evolution (DE) algorithm is presented and applied to global optimization in this research. DE suggested initially fur the solution to Chebychev polynomial fitting problem is similar to genetic algorithm(GA) including crossover, mutation and selection process. However, differential evolution algorithm is simpler than GA because it uses a vector concept in populating process. And DE turns out to be converged faster than CA, since it employs the difference information as pseudo-sensitivity In this paper, a trial vector and its control parameters of DE are examined and unconstrained optimization problems of highly nonlinear multimodal functions are demonstrated. To illustrate the efficiency of DE, convergence rates and robustness of global optimization algorithms are compared with those of simple GA.

• Industrial Engineering and Management Systems
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• 제14권1호
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• pp.11-21
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• 2015

The purpose of this research is to develop algorithms using the Differential Evolution Algorithm (DE) to solve a multi-objective, sources and stages location-allocation problem. The development process starts from the design of a standard DE, then modifies the recombination process of the DE in order improve the efficiency of the standard DE. The modified algorithm is called modified DE. The proposed algorithms have been tested with one real case study (large size problem) and 2 randomly selected data sets (small and medium size problems). The computational results show that the modified DE gives better solutions and uses less computational time than the standard DE. The proposed heuristics can find solutions 0 to 3.56% different from the optimal solution in small test instances, while differences are 1.4-3.5% higher than that of the lower bound generated by optimization software in medium and large test instances, while using more than 99% less computational time than the optimization software.

• Journal of Communications and Networks
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• 제16권5호
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• pp.523-533
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• 2014

Orthogonal frequency division multiple access (OFDMA) is a promising technique, which can provide high downlink capacity for the future wireless systems. The total capacity of OFDMA can be maximized by adaptively assigning subchannels to the user with the best gain for that subchannel, with power subsequently distributed by water-filling. In this paper, we propose the use of composite differential evolution (CoDE) algorithm to allocate the subchannels. The CoDE algorithm is population-based where a set of potential solutions evolves to approach a near-optimal solution for the problem under study. CoDE uses three trial vector generation strategies and three control parameter settings. It randomly combines them to generate trial vectors. In CoDE, three trial vectors are generated for each target vector unlike other differential evolution (DE) techniques where only a single trial vector is generated. Then the best one enters the next generation if it is better than its target vector. It is shown that the proposed method obtains higher sum capacities as compared to that obtained by previous works, with comparable computational complexity.

• Industrial Engineering and Management Systems
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• 제10권3호
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• pp.203-208
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• 2011

Job shop scheduling is well-known as one of the hardest combinatorial optimization problems and has been demonstrated to be NP-hard problem. In the past decades, several researchers have devoted their effort to develop evolutionary algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for job shop scheduling problem. Differential Evolution (DE) algorithm is a more recent evolutionary algorithm which has been widely applied and shown its strength in many application areas. However, the applications of DE on scheduling problems are still limited. This paper proposes a one-stage differential evolution algorithm (1ST-DE) for job shop scheduling problem. The proposed algorithm employs random key representation and permutation of m-job repetition to generate active schedules. The performance of proposed method is evaluated on a set of benchmark problems and compared with results from an existing PSO algorithm. The numerical results demonstrated that the proposed algorithm is able to provide good solutions especially for the large size problems with relatively fast computing time.

• Journal of Electrical Engineering and Technology
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• 제4권3호
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• pp.323-329
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• 2009

This paper presents a new approach for thermal unit commitment (UC) using a differential evolution (DE) algorithm. DE is an effective, robust, and simple global optimization algorithm which only has a few control parameters and has been successfully applied to a wide range of optimization problems. However, the standard DE cannot be applied to binary optimization problems such as UC problems since it is restricted to continuous-valued spaces. This paper proposes binary differential evolution (BDE), which enables the DE to operate in binary spaces and applies the proposed BDE to UC problems. Furthermore, this paper includes heuristic-based constraint treatment techniques to deal with the minimum up/down time and spinning reserve constraints in UC problems. Since excessive spinning reserves can incur high operation costs, the unit de-commitment strategy is also introduced to improve the solution quality. To demonstrate the performance of the proposed BDE, it is applied to largescale power systems of up to 100-units with a 24-hour demand horizon.

• 한국지능시스템학회논문지
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• 제12권5호
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• pp.451-461
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• 2002

차분 진화는 다양한 형태의 목적함수를 최적화하는데 매우 효율적인 방법임이 입증되었다 차분 진화의 가장 큰 이점은 개념적 단순성과 사용의 용이성이다. 그러나 차분 진화의 수렴성이 제어 파라미터에 매우 민감한 단점이 있다. 본 논문은 새로운 교배용 벡터 생성법과 제어 파라미터의 적응 메커니즘을 결합한 적응성 있는 차분 진화를 제안한다. 이는 수렴성을 해치지 않으면서 차분 진화를 보다 강인하게 만들며 사용이 쉽도록 해준다. 12가지 최적화 문제에 대해 제안한 방법을 시험하였다. 적응성 있는 차분 진화의 응용 사례로써 이벤트 예측을 위한 교사 클러스터링 방법을 제안한다. 이 방법을 진화에 의한 이벤트 클러스터링이라 부르며 데이터 모델링 검증에 널리 사용되는 4 가지 사례에 대해 그 성능을 시험하였다.

• Journal of information and communication convergence engineering
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• 제17권4호
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• pp.261-266
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• 2019

Differential evolution (DE) is an uncomplicated and serviceable developmental algorithm. Nevertheless, its execution depends on strategies and regulating structures. The combination of several strategies between subpopulations helps to stabilize the probing on DE. In this paper, we propose a unique k-mean soft island model DE(KSDE) algorithm which maintains population diversity through soft island model (SIM). A combination of various approaches, called KSDE, intended for migrating the subpopulation information through SIM is developed in this study. First, the population is divided into k subpopulations using the k-means clustering algorithm. Second, the mutation pattern is singled randomly from a strategy pool. Third, the subpopulation information is migrated using SIM. The performance of KSDE was analyzed using 13 benchmark indices and compared with those of high-technology DE variants. The results demonstrate the efficiency and suitability of the KSDE system, and confirm that KSDE is a cost-effective algorithm compared with four other DE algorithms.

• Journal of Electrical Engineering and Technology
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• 제8권4호
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• pp.686-693
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• 2013

Generation Expansion Planning (GEP) is one of the most important decision-making activities in electric utilities. Least-cost GEP is to determine the minimum-cost capacity addition plan (i.e., the type and number of candidate plants) that meets forecasted demand within a pre specified reliability criterion over a planning horizon. In this paper, Differential Evolution (DE), and Opposition-based Differential Evolution (ODE) algorithms have been applied to the GEP problem. The original GEP problem has been modified by incorporating Virtual Mapping Procedure (VMP). The GEP problem of a synthetic test systems for 6-year, 14-year and 24-year planning horizons having five types of candidate units have been considered. The results have been compared with Dynamic Programming (DP) method. The ODE performs well and converges faster than DE.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2022년도 제65차 동계학술대회논문집 30권1호
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• pp.391-392
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• 2022

본 논문에서는 SparkHDE-EM이라는 생태학적 모델 알고리즘에 기반한 하이브리드 DE를 제안한다. 그리고 Spark 기반 아일랜드 모델을 도입하여 다양한 DE 변종의 병렬화를 구현한다. 또한 Monod 모델을 활용하여 자원 간의 균형을 유지하는 방법을 제안한다.

• Journal of Electrical Engineering and Technology
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• 제7권5호
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• pp.681-688
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• 2012

This paper presents the application of Differential Evolution (DE) algorithm to obtain a solution for Bid Based Dynamic Economic Dispatch (BBDED) problem including the transmission losses and to maximize the social profit in a deregulated power system. The IEEE-30 bus test system with six generators, two customers and two trading periods are considered under various bidding strategies in a day-ahead electricity market. By matching the bids received from supplying and distributing entities, the Independent System Operator (ISO) maximize the social profit, (with the choices available). The simulation results of DE are compared with the results of Particle swarm optimization (PSO). The results demonstrate the potential of DE algorithm and show its effectiveness to solve BBDED.