• International Journal of Computer Science & Network Security
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• v.23 no.6
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• pp.193-201
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• 2023

In this paper, we consider the maximum scatter traveling salesman problem (MSTSP), a travelling salesman problem (TSP) variant. The problem aims to maximize the minimum length edge in a salesman's tour that travels each city only once in a network. It is a very complicated NP-hard problem, and hence, exact solutions can be found for small sized problems only. For large-sized problems, heuristic algorithms must be applied, and genetic algorithms (GAs) are found to be very successfully to deal with such problems. So, this paper develops a hybrid GA (HGA) for solving the problem. Our proposed HGA uses sequential sampling algorithm along with 2-opt search for initial population generation, sequential constructive crossover, adaptive mutation, randomly selected one of three local search approaches, and the partially mapped crossover along with swap mutation for perturbation procedure to find better quality solution to the MSTSP. Finally, the suggested HGA is compared with a state-of-art algorithm by solving some TSPLIB symmetric instances of many sizes. Our computational experience reveals that the suggested HGA is better. Further, we provide solutions to some asymmetric TSPLIB instances of many sizes.

• International Journal of Computer Science & Network Security
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• v.24 no.3
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• pp.12-22
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• 2024

The travelling salesman problem is very famous and very difficult combinatorial optimization problem that has several applications in operations research, computer science and industrial engineering. As the problem is difficult, finding its optimal solution is computationally very difficult. Thus, several researchers have developed heuristic/metaheuristic algorithms for finding heuristic solutions to the problem instances. In this present study, a new hybrid genetic algorithm (HGA) is suggested to find heuristic solution to the problem. In our HGA we used comprehensive sequential constructive crossover, adaptive mutation, 2-opt search and a new local search algorithm along with a replacement method, then executed our HGA on some standard TSPLIB problem instances, and finally, we compared our HGA with simple genetic algorithm and an existing state-of-the-art method. The experimental studies show the effectiveness of our proposed HGA for the problem.

• Journal of the Korean Society for Library and Information Science
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• v.53 no.2
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• pp.29-48
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• 2019

The free learning semester system focuses on the activation of career education of middle school students as an educational policy that links career recognition in elementary school, career search in middle school, and career planning in high school. This system was fully implemented in 2016 and public libraries also provided various programs. This study analyzed the free learning semester system programs of public libraries in Busan city and demonstrated the contribution of local development. As a result, career and job search, career exploration and experience, and information literacy enhancement programs contributed to local knowledge culture, reading culture, learning culture, living culture and leisure culture. However, contribution of reading exhibitions, job experience, information literacy enhancement to the leisure culture and local economy were limited. Therefore, it is desirable that all libraries should add programs related to knowledge ecosystem structure, digital information gap, human healing, social environment issues, future job prospects, and provide information literacy programs.

• Journal of Computational Design and Engineering
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• v.3 no.4
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• pp.295-311
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• 2016

This paper addresses to the flowshop scheduling problem with blocking constraints. The objective is to minimize the makespan criterion. We propose a hybrid combinatorial particle swarm optimization algorithm (HCPSO) as a resolution technique for solving this problem. At the initialization, different priority rules are exploited. Experimental study and statistical analysis were performed to select the most adapted one for this problem. Then, the swarm behavior is tested for solving a combinatorial optimization problem such as a sequencing problem under constraints. Finally, an iterated local search algorithm based on probabilistic perturbation is sequentially introduced to the particle swarm optimization algorithm for improving the quality of solution. The computational results show that our approach is able to improve several best known solutions of the literature. In fact, 76 solutions among 120 were improved. Moreover, HCPSO outperforms the compared methods in terms of quality of solutions in short time requirements. Also, the performance of the proposed approach is evaluated according to a real-world industrial problem.

• Journal of Computing Science and Engineering
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• v.8 no.4
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• pp.199-206
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• 2014

The ant colony optimization (ACO) algorithm is a new heuristic algorithm that offers good robustness and searching ability. With in-depth exploration, the ACO algorithm exhibits slow convergence speed, and yields local optimization solutions. Based on analysis of the ACO algorithm and the genetic algorithm, we propose a novel hybrid genetic ant colony optimization (NHGAO) algorithm that integrates multi-population strategy, collaborative strategy, genetic strategy, and ant colony strategy, to avoid the premature phenomenon, dynamically balance the global search ability and local search ability, and accelerate the convergence speed. We select the traveling salesman problem to demonstrate the validity and feasibility of the NHGAO algorithm for solving complex optimization problems. The simulation experiment results show that the proposed NHGAO algorithm can obtain the global optimal solution, achieve self-adaptive control parameters, and avoid the phenomena of stagnation and prematurity.

• IE interfaces
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• v.17 no.spc
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• pp.103-110
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• 2004

This paper presents a two-phase method for the vehicle routing problems with time windows(VRPTW). In a supply chain management(SCM) environment, timely distribution is very important problem faced by most industries. The VRPTW is associated with SCM for each customer to be constrained the time of service. In the VRPTW, the objective is to design the least total travel time routes for a fleet of identical capacitated vehicles to service geographically scattered customers with pre-specified service time windows. The proposed approach is based on ant colony optimization(ACO) and improvement heuristic. In the first phase, an insertion based ACO is introduced for the route construction and its solutions is improved by an iterative random local search in the second phase. Experimental results show that the proposed two-phase method obtains very good solutions with respect to total travel time minimization.

• Journal of Communications and Networks
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• v.8 no.1
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• pp.85-92
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• 2006

In this paper, we study a wireless cellular network design problem. It consists of selecting the location of the base station controllers and mobile service switching centres, selecting their types, designing the network into a tree-topology, and selecting the link types, while considering the location and the demand of base transceiver stations. We propose a constraint programming model and develop a heuristic combining local search and constraint programming techniques to find very good solutions in a reasonable amount of time for this category of problem. Numerical results show that our approach, on average, improves the results from the literature.

• Journal of Korean Institute of Industrial Engineers
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• v.35 no.2
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• pp.160-170
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• 2009

This paper studies a vehicle routing problem variant which considers customers to require simultaneous delivery and pick-up under time windows(VRPSDP-TW). The objective of this paper is to minimize the total travel distance of routes that satisfy both the delivery and pick-up demand. We propose a heuristic algorithm for solving the VRPSDP-TW, based on the ant colony system(ACS). In route construction, an insertion algorithm based ACS is applied and the interim solution is improved by local search. Through iterative processes, the heuristic algorithm drives the best solution. Experiments are implemented to evaluate a performance of the algorithm on some test instances from literature.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.26 no.9B
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• pp.1299-1307
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• 2001

고정된 패턴을 사용하는 기존의 고속 블록기반 움직임 추정법에서는 국소 최소해로 고립될 가능성이 있을 뿐만 아니라, 여러 움직임이 공존하는 움직임 경계에서 정확한 움직임의 추정이 어렵다는 문제점을 가지고 있다. 이러한 문제점을 극복하기 위하여 본 논문에서는 탐색점의 수를 줄이는 동시에 국소 최소해로의 고립을 피하기 위하여 탐색 후보영역을 적용한 다중 국소 탐색법(multiple local search method : MLSM)을 제안한다. 또한, 블록 내의 움직임 영역별 정합오차의 최소편차를 고려하는 새로운 정합함수를 제안함으로써 움직임 경계에서 움직임 벡터추정의 부정확성과 움직임 보상영상에서의 화질저하문제를 개선하고자 한다. 실험결과, 제안한 방법은 기존의 방법에 비해 움직임 경계에서의 추정에서 우수한 결과를 보였으며, PSNR에 대해서도 전역탐색법과 유사한 결과를 얻을 수 있었고, 움직임 보상결과, 움직임 경계부근에서의 향상된 화질을 얻을 수 있었다.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.490-490
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• 2000

Parallel genetic algorithms are particularly easy to implement and promise substantial gains in performance. Its basic idea is to keep several subpopulations that are processed by genetic algorithms. Furthermore, a migration mechanism produces a chromosome exchange between subpopulation. In this paper, a new selection method based on non-linear fitness assignment presented. The use of proposed ranking selection permits higher local exploitation search, where the diversity of populations is structure. Experimental results show that the relation between local-global search balance and the probabilities of reaching a desired solution.