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

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.

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.

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.

Evolutionary Analysis for Continuous Search Space

이준성(Joon-Seong Lee),배병규(Byeong-Gyu Bae) 한국지능시스템학회 2011 한국지능시스템학회논문지 Vol.21 No.2

본 논문에서는 연속적인 파라미터 공간에 대한 최적화에 대해 진화적 알고리즘의 특징적인 형상화를 제시한다. 이 방법은유전알고리즘이 연속적인 탐색공간에서의 파라미터 식별에 대해 가장 강점을 지녔다는 점에 착안한 것이다. 유전알고리즘과 제안한 알고리즘과의 주요한 차이점은 개별적 또는 연속적인 묘사의 차이가 있다는 것이다. 잘 알려진 실험함수의 최적화문제를 도입하여 연속 탐색공간 문제에 대해 제안하는 알고리즘에 대해 계산시간 및 사용메모리 등의 성능이 우수하다는 효율성을 보였다. In this paper, the evolutionary algorithm was specifically formulated for optimization with continuous parameter space. The proposal was motivated by the fact that the genetic algorithms have been most intensively reported for parameter identification problems with continuous search space. The difference of primary characteristics between genetic algorithms and the proposed algorithm, discrete or continuous individual representation has made different areas to which the algorithms should be applied. Results obtained by optimization of some well-known test functions indicate that the proposed algorithm is superior to genetic algorithms in all the performance, computation time and memory usage for continuous search space problems.

청소년기 준가임성의 진화적 가설

김민욱,박한선 대한체질인류학회 2025 해부·생물인류학 (Anat Biol Anthropol) Vol.38 No.2

본 논문은 인간과 일부 유인원에서 관찰되는 청소년기 준가임성 현상을 체질인류학적 및 진화생물학적 관점에서 고찰한다. 인간은 만숙성 특성으로 인해 성체로 성장하는 데 많은 에너지와 시간이 필요하며, 초경과 최적 임신·출산 시기가 불일치하는 독특한 발달 패턴을 보인다. 이로 인해 신체적 준비가 완전하지 않은 상태에서도 일정 기간 임신 가능성이 내포된 ‘준가임성’ 현상이 발생한다. 본 연구에서는 시상하부-뇌하수체-난소 (HPO) 축의 미성숙과 불완전한 호르몬 피드백, 그리고 영양 상태 및 대사 장애와 같은 발달생리학적 요인을 중심으로 준가임성의 기전을 검토하고, 수렵채집 사회와 유인원 사례를 통해 이 현상의 보편성을 논의한다. 또한, 평균 성인 여성 체중, 초경 연령, 첫 출산 연령, 15세까지의 생존 확률 등 생애사적 변수가 준가임성 기간에 미치는 영향을 예비적으로 분석하여 제시한다. 이를 통해서 성장-번식 트레이드오프, 포괄적합도 향상, 초보자 짝 탐색이라는 세 가지 진화적 가설을 제시한다. 청소년기 준가임성 현상을 진화적으로 설명하기 위해서는 향후 수렵채집인을 비롯한 다양한 인구집단 연구와 수리적 모델링을 통한 체질인류학적 연구가 필요하다. This paper examines the phenomenon of adolescent subfecundity in humans and some great apes from the perspectives of biological anthropology and evolutionary biology. Owing to their altricial nature, humans require considerable energy and time to reach full maturity, and they exhibit a unique developmental pattern in which the onset of menarche and the optimal timing for reproduction (i.e., pregnancy and childbirth) are misaligned. Consequently, a period emerges during which reproductive capability is only partially realized despite incomplete physical readiness. In this study, we review the developmental physiological mechanisms underlying subfecundity, with particular emphasis on the immaturity of the hypothalamic-pituitary-ovarian (HPO) axis, incomplete hormonal feedback regulation, and metabolic disturbances related to nutritional status and energy balance. We further discuss the prevalence of this phenomenon by drawing on evidence from hunter-gatherer societies and research on great apes. Additionally, by preliminarily analyzing the influence of life history variables-such as average adult female body mass, age at menarche, age at first reproduction, and survival probability until age 15-on the duration of subfecundity, we propose three evolutionary hypotheses: the growth-reproduction trade-off hypothesis, the inclusive fitness enhancement hypothesis, and the beginner mate-search hypothesis. A comprehensive understanding of adolescent subfecundity from an evolutionary perspective will require further research involving diverse populations, including hunter-gatherers, as well as mathematical modeling within a biological anthropology framework.