A Hybrid of Evolutionary Search and Local Heuristic Search for Combinatorial Optimization Problems

Park, Lae-Jeong,Park, Cheol-Hoon Korean Institute of Intelligent Systems 2001 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.1 No.1

Evolutionary algorithms(EAs) have been successfully applied to many combinatorial optimization problems of various engineering fields. Recently, some comparative studies of EAs with other stochastic search algorithms have, however, shown that they are similar to, or even are not comparable to other heuristic search. In this paper, a new hybrid evolutionary algorithm utilizing a new local heuristic search, for combinatorial optimization problems, is presented. The new intelligent local heuristic search is described, and the behavior of the hybrid search algorithm is investigated on two well-known problems: traveling salesman problems (TSPs), and quadratic assignment problems(QAPs). The results indicate that the proposed hybrid is able to produce solutions of high quality compared with some of evolutionary and simulated annealing.

Study on Thinking Evolution based ant Colony Algorithm in Typical Production Scheduling Application

Xianmin Wei,Peng Zhang 보안공학연구지원센터 2015 International Journal of Hybrid Information Techno Vol.8 No.6

Aiming at solving the NP-hard workshop production scheduling problems, proposed one kind based on mind evolutionary algorithm. The algorithm in the traditional ant colony algorithm is established, and the combination of evolutionary thought and local optimization idea overcomes the basic ant colony algorithm is easy to fall into local optimal defects, the improved state transition rules, defining a pheromone range, improve the pheromone update strategy, and the increase of neighborhood search. Experimental results show that, for a typical production scheduling problems, based on mind evolutionary ant colony algorithm can obtain the optimal solution in theory, optimal solution, the solution and average three indicators are better than the basic ant colony algorithm, showed good performance.

Face Recognition Using Harmony Search-Based Selected Features

Rana Sawalha,Iyad Abu Doush 보안공학연구지원센터 2012 International Journal of Hybrid Information Techno Vol.5 No.2

Development of Pareto strategy multi-objective function method for the optimum design of ship structures

Na, Seung-Soo,Karr, Dale G. The Society of Naval Architects of Korea 2016 International Journal of Naval Architecture and Oc Vol.8 No.6

It is necessary to develop an efficient optimization technique to perform optimum designs which have given design spaces, discrete design values and several design goals. As optimization techniques, direct search method and stochastic search method are widely used in designing of ship structures. The merit of the direct search method is to search the optimum points rapidly by considering the search direction, step size and convergence limit. And the merit of the stochastic search method is to obtain the global optimum points well by spreading points randomly entire the design spaces. In this paper, Pareto Strategy (PS) multi-objective function method is developed by considering the search direction based on Pareto optimal points, the step size, the convergence limit and the random number generation. The success points between just before and current Pareto optimal points are considered. PS method can also apply to the single objective function problems, and can consider the discrete design variables such as plate thickness, longitudinal space, web height and web space. The optimum design results are compared with existing Random Search (RS) multi-objective function method and Evolutionary Strategy (ES) multi-objective function method by performing the optimum designs of double bottom structure and double hull tanker which have discrete design values. Its superiority and effectiveness are shown by comparing the optimum results with those of RS method and ES method.

Development of Pareto strategy multi-objective function method for the optimum design of ship structures

나승수,Dale G. Karr 대한조선학회 2016 International Journal of Naval Architecture and Oc Vol.8 No.6

It is necessary to develop an efficient optimization technique to perform optimum designs which have given design spaces, discrete design values and several design goals. As optimization techniques, direct search method and stochastic search method are widely used in designing of ship structures. The merit of the direct search method is to search the optimum points rapidly by considering the search direction, step size and convergence limit. And the merit of the stochastic search method is to obtain the global optimum points well by spreading points randomly entire the design spaces. In this paper, Pareto Strategy (PS) multi-objective function method is developed by considering the search direction based on Pareto optimal points, the step size, the convergence limit and the random number generation. The success points between just before and current Pareto optimal points are considered. PS method can also apply to the single objective function problems, and can consider the discrete design variables such as plate thickness, longitudinal space, web height and web space. The optimum design results are compared with existing Random Search (RS) multi-objective function method and Evolutionary Strategy (ES) multi-objective function method by performing the optimum designs of double bottom structure and double hull tanker which have discrete design values. Its superiority and effectiveness are shown by comparing the optimum results with those of RS method and ES method.

자원과 능력이 미흡한 상황에서의 원거리 탐색(Distant Search)과 최고경영자의 역할에 관한 탐색적 연구

남윤성,박철순,권기환 한국전문경영인학회 2011 專門經營人硏究 Vol.14 No.3

This research investigates that distant search for achieving organizational knowledge can be one possible solution under condition when there are scarce resource and capability, through grafting resource-based theory and evolutionary theory. For this reason, we focus on private electric company case study in Korea. When there are scarce resource and capability, it is not only impossible to conduct local search which is based on existing resource and capability but also it has negative effect that it lets a firm retain routines which should be selected. Thus, it is necessary to conduct distant search which is unrelated to existing resource and capability. And CEOs need to pay attention to invisible activities to accumulate resource and capability of their firms. This research has implication that it tries exploratory study that distant search is needed when there are scarce resource and capability.

Harmony Search 알고리즘 기반 군집로봇의 행동학습 및 진화

김민경(Min-Kyung Kim),고광은(Kwang-Eun Ko),심귀보(Kwee-Bo Sim) 한국지능시스템학회 2010 한국지능시스템학회논문지 Vol.20 No.3

군집 로봇시스템에서 개개의 로봇은 스스로 주위의 환경과 자신의 상태를 스스로 판단하여 행동하고, 필요에 따라서는 다른 로봇과 협조를 통하여 임의의 주어진 임무를 수행할 수 있어야 한다. 따라서 각 로봇 개체는 동적으로 변화하는 환경에 잘 적응할 수 있도록 하기 위한 학습 및 진화능력을 갖는 것이 필수적이다. 이를 위하여 본 논문에서는 Q-learning 알고리즘을 기반으로 하는 학습과 Harmony Search 알고리즘을 이용한 진화방법을 제안하였으며, 유전 알고리즘이 아닌 Harmony Search 알고리즘을 제안함으로써 정확도를 높이고자 하였다. 그 결과를 이용하여 군집 로봇의 로봇 개체 환경변화에 따른 임무 수행 능력의 향상을 검증한다. Each robot decides and behaviors themselves surrounding circumstances in the swarm robot system. Robots have to conduct tasks allowed through cooperation with other robots. Therefore each robot should have the ability to learn and evolve in order to adapt to a changing environment. In this paper, we proposed learning based on Q-learning algorithm and evolutionary using Harmony Search algorithm and are trying to improve the accuracy using Harmony Search Algorithm, not the Genetic Algorithm. We verify that swarm robot has improved the ability to perform the task.

A Nonlinear System Identification based on Additive Expression Tree Model with Cuckoo Search

Bin Yang 보안공학연구지원센터 2015 International Journal of Hybrid Information Techno Vol.8 No.8

In this paper, an efficient approach of combining additive expression tree model (AET) with hybrid evolutionary method is proposed to identify nonlinear systems. As linear variant of additive tree model, additive expression tree model is proposed to encode the mathematical formulations. For finding the optimal structure and parameters of systems, a hybrid evolutionary method integrating a new structure based evolutionary algorithm and cuckoo search is employed. We illustrate some experimental comparisons with neural network, neural network integrating fuzzy system and symbolic regression methods. Experimental results reveal that our model and optimization method perform better.

하모니 서치 알고리즘의 피치 조정 연산자 개선 연구

이건희,김종우 한국지능시스템학회 2022 한국지능시스템학회논문지 Vol.32 No.4

본 논문에서는 하모니 서치 알고리즘의 성능, 즉 전역 최적해로의 수렴성을 높이기 위한 연구의 일환으로서, 하모니 서치 알고리즘의 주요 3연산자(무작위 선택, 하모니 메모리 고려,피치 조정) 중의 하나인 피치 조정의 방식을 수정해보았다. 5개의 테스트 함수를 통해 시뮬레이션 해 본 결과, 기존 피치 조정 방식의 하모니 서치 알고리즘보다 4개의 테스트 함수에서 확실히 더 좋은 결과를 보였으며, 나머지 1개의 테스트 함수에서는 두 방식간의 차이가거의 없었다

Constitutive Parameter Identification of Inelastic Equations Using an Evolutionary Algorithm

이은철(Eun-Chul Lee),이준성(Joon-Seong Lee),古川知成(Tomonari Hurukawa) 한국지능시스템학회 2009 한국지능시스템학회논문지 Vol.19 No.1

본 논문에서는 제안된 진화적 알고리즘을 바탕으로 한 비탄성 구성방정식의 파라미터를 결정하기 위한 방법을 제시한다. 이 방법의 장점은 오차를 갖고 있는 측정된 데이터들이나 모델 방정식들이 부정확하더라도 적절한 파라미터들이 결정되어진다는 것이다. 실험설계는 단축하중과 일정 온도조건하의 샤보쉬 재료모델의 파라미터 결정에 적합하였다. 동시에 모델의 파라미터들은 실험데이터들과 제안한 방법에 의한 값들과 일치하였다. 다른 방법들에 의한 값들과 비교해 본 결과, 제안한 방법에 의한 응력-변형률 선도는 실제적인 재료거동에 비해 좋게 나타났다. This paper presents a method for identifying the parameter set of inelastic constitutive equations, which is based on an Evolutionary Algorithm. The advantage of the method is that appropriate parameters can be identified even when the measured data are subject to considerable errors and the model equations are inaccurate. The design of experiments suited for the parameter identification of a material model by Chaboche under the uniaxial loading and stationary temperature conditions was first considered. Then the parameter set of the model was identified by the proposed method from a set of experimental data. In comparison to those by other methods, the resultant stress-strain curves by the proposed method correlated better to the actual material behaviors.