• Industrial Engineering and Management Systems
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• 제14권1호
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• pp.44-57
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• 2015

In recent years, for the purpose of solving the problem regarding environment protection and resource saving, certain measures and policies have been promoted to establish a reverse supply chains (RSCs) with material flows from collection of used products to reuse the recycled parts in production of products. It is necessary to analyze behaviors of RSC members to determine the optimal operation. This paper discusses a RSC with a retailer and a manufacturer and verifies the behavior strategies of RSC members which may change over time in response to changes parameters related to the recycling promotion activity in RSC. A retailer takes two behaviors: cooperation/non-cooperation in recycling promotion activity. A manufacturer takes two behaviors: monitoring/non-monitoring of behaviors of the retailer. Evolutionary game theory combining the evolutionary theory of Darwin with game theory is adopted to clarify analytically evolutionary outcomes driven by a change in each behavior of RSC members over time. The evolutionary stable strategies (ESSs) for RSC members' behaviors are derived by using the replicator dynamics. The analysis numerically demonstrates how parameters of the recycling promotion activity: (i) sale promotion cost, (ii) monitoring cost, (iii) compensation and (iv) penalty cost affect the judgment of ESSs of behaviors of RSC members.

• Journal of Microbiology and Biotechnology
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• 제17권11호
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• pp.1880-1884
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• 2007

This study was conducted to find the optimum extraction condition of Gold-Thread for antibacterial activity against Streptococcus mutans using The evolutionary operation-factorial design technique. Higher antibacterial activity was achieved in a higher extraction temperature ($R^2=-0.79$) and in a longer extraction time ($R^2=-0.71$). Antibacterial activity was not affected by differentiation of the ethanol concentration in the extraction solvent ($R^2=-0.12$). The maximum antibacterial activity of clove against S. mutans determined by the EVOP-factorial technique was obtained at $80^{\circ}C$ extraction temperature, 26 h extraction time, and 50% ethanol concentration. The population of S. mutans decreased from 6.110 logCFU/ml in the initial set to 4.125 logCFU/ml in the third set.

• 한국지능시스템학회논문지
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• 제16권5호
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• pp.594-600
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• 2006

본 논문은 전기철도차량의 운행시간 여유분을 고려하여 에너지 소비를 최소화하는 경제운전 패턴을 찾는 방안을 제시하였다. 경제최고속도와 타행끝점속도를 주행패턴의 변수로 사용하여 퍼지모델을 구축하고 이를 대상으로 진화 탐색을 적용하여 최적의 경제운전 패턴을 찾아낼 수 있으며, 사례연구를 통해 이를 입증하였다.

• 한국멀티미디어학회논문지
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• 제12권2호
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• pp.290-299
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• 2009

진화하는 인공신경망은 인공지능분야와 게임 NPC의 지능 설계 분야에서 새롭게 각광을 받고 있다. 하지만 진화하는 인공신경 망을 이용하여 게임 NPC의 지능을 설계할 때 인공신경 망의 구조가 복잡함에 따라 진화와 평가에 필요한 연산량이 크며 또한 적절한 적합도 함수를 설계하지 못하면 지능적인 NPC를 설계할 수 없는 등의 문제점을 가지고 있다. 본 논문에서는 이러한 문제들을 해결하고자 동적 상태 진화 인공신경망을 제안한다. 동적 상태 진화 인공신경망은 전통적인 진화하는 인공신경망 알고리즘에 기반하여 진화 과정에서 신경망의 신경세포들 사이의 시냅스를 제거(disabled) 하거나 고정(fixed)시키는 방법을 통하여 진화와 평가과정에 소모되는 연산량을 줄이는 알고리즘이다. 본 논문은 Darwin Platform 을 테스트 베드로 축구게임 NPC의 지능 설계를 통하여 제안하는 방법의 유용성을 검증한다.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회/대한산업공학회 2003년도 춘계공동학술대회
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• pp.1071-1076
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• 2003

Evolutionary algorithm is recognized as a promising approach to solving multi-objective combinatorial optimization problems. When no preference information of decision makers is given, multi-objective optimization problems have been commonly used to search for diverse and good Pareto optimal solution. In this paper we propose a new multi-objective evolutionary algorithm based on competitive coevolutionary algorithm, and demonstrate the applicability of the algorithm. The proposed algorithm is designed to promote both population diversity and rapidity of convergence. To achieve this, the strategies of fitness evaluation and the operation of the Pareto set are developed. The algorithm is applied to job shop scheduling problems (JSPs). The JSPs have two objectives: minimizing makespan and minimizing earliness or tardiness. The proposed algorithm is compared with existing evolutionary algorithms in terms of solution quality and diversity. The experimental results reveal the effectiveness of our approach.

• 제어로봇시스템학회논문지
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• 제7권4호
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• pp.295-303
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• 2001

The demand for steam generators is increasing in industrial systems in which the design strategy should be implemented for safe and efficient operation of steam generators. It is, however, difficult to design a controller by the conventional method because of the nonlinear dynamics of the steam generator and influences by the set value of disturbance. This paper presents an automatic parameter optimization technique for a multivariable fuzzy controller using evolutionary strategy, At first, we use the steady state information such as a steady state gain matrix(SSGM) and a relative gain matrix(RGM). We can obtain much information on the control inputs and the outputs of the boiler-turbine system from the matrices. In order to determine the structure of the controller by using RGM and SSGM, the fuzzy rules are trained by evolutionary strategy. The good performance of the proposed multivariable fuzzy controller is verified through simulations.

• 전기학회논문지
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• 제58권6호
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• pp.1057-1064
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• 2009

This paper introduces a new approach based on a quantum-inspired evolutionary algorithm (QEA) to solve unit commitment (UC) problems. The UC problem is a complicated nonlinear and mixed-integer combinatorial optimization problem with heavy constraints. This paper proposes a bounded quantum evolutionary algorithm (BQEA) to effectively solve the UC problems. The proposed BQEA adopts both the bounded rotation gate, which is simplified and improved to prevent premature convergence and increase the global search ability, and the increasing rotation angle approach to improve the search performance of the conventional QEA. Furthermore, it includes heuristic-based constraint treatment techniques to deal with the minimum up/down time and spinning reserve constraints in the UC problems. Since the excessive spinning reserve 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 BQEA, it is applied to the large-scale power systems of up to 100-unit with 24-hour demand.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권9호
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• pp.2976-2990
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• 2022

The adaptive adjustment of reference or weight vectors in decomposition-based methods has been a hot research topic in the evolutionary community over the past few years. Although various methods have been proposed regarding this issue, most of them aim to diversify solutions in the objective space to cover the true Pareto fronts as much as possible. Different from them, this paper proposes a knee point-based reference vector adaptive adjustment strategy to concurrently balance the convergence and diversity. To be specific, the knee point-based reference vector adaptive adjustment strategy firstly utilizes knee points to construct the adaptive reference vectors. After that, a new fitness function is defined mathematically. Then, this paper further designs a many-objective evolutionary algorithm with knee point-based reference vector adaptive adjustment strategy, where the mating operation and environmental selection are designed accordingly. The proposed method is extensively tested on the WFG test suite with 8, 10 and 12 objectives and MPDMP with state-of-the-art optimizers. Extensive experimental results demonstrate the superiority of the proposed method over state-of-the-art optimizers and the practicability of the proposed method in tackling practical many-objective optimization problems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권1호
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• pp.80-96
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• 2022

Nowadays, artificial intelligence promotes the rapid development of skin cancer detection technology, and the federated skin cancer detection model (FSDM) and dual generative adversarial network model (DGANM) solves the fragmentation and privacy of data to a certain extent. To overcome the problem that the many-objective evolutionary algorithm (MaOEA) cannot guarantee the convergence and diversity of the population when solving the above models, a many-objective evolutionary algorithm based on integrated strategy (MaOEA-IS) is proposed. First, the idea of federated learning is introduced into population mutation, the new parents are generated through sub-populations employs different mating selection operators. Then, the distance between each solution to the ideal point (SID) and the Achievement Scalarizing Function (ASF) value of each solution are considered comprehensively for environment selection, meanwhile, the elimination mechanism is used to carry out the select offspring operation. Eventually, the FSDM and DGANM are solved through MaOEA-IS. The experimental results show that the MaOEA-IS has better convergence and diversity, and it has superior performance in solving the FSDM and DGANM. The proposed MaOEA-IS provides more reasonable solutions scheme for many scholars of skin cancer detection and promotes the progress of intelligent medicine.

• 생명과학회지
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• 제18권9호
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• pp.1284-1289
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• 2008

본 연구에서는 EVOP-factorial system을 활용하여 매실식초의 최적 발효 조건을 찾고자 하였다. 3-7% 사이의 에탄올 농도(r=-0.5166)와 glucose 농도(r=-0.5061)는 10% 유의수준에서 산도에 영향을 미치는 것으로 확인되었다. 24-$33^{\circ}C$의 범위 내에서 발효온도는 매실식초의 산도 증가에 유의적인 영향을 미치지 못하는 것으로 확인되었다(r=0.1082). EVOP- factorial system을 활용하여 얻은 매실식초의 최적 발효조건은 발효 온도: $30^{\circ}C$, 에탄올 농도: 4%, 포도당 농도: 0.2%로 결정되었으며, 최적 조건에서의 산도값은 6.365%로 set 1의 중심점에서 나타난 산도값 5.4%에 비해 1.0%정도 높아졌다. 본 연구결과는 EVOP-factorial design을 이용하여 매실식초의 최적 발효 조건을 확인한 최초의 시도이다.