• International Journal of Fuzzy Logic and Intelligent Systems
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• v.1 no.1
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• pp.6-12
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• 2001

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.

• Structural Engineering and Mechanics
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• v.65 no.6
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• pp.707-719
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• 2018

In this article, a new method is introduced to improve the local search capability of meta-heuristic algorithms using the projection of the path on the border of constraints. In a mathematical point of view, the Gradient Projection Method is applied through a new approach, while the imposed limitations are removed. Accordingly, the gradient vector is replaced with a new meta-heuristic based vector. Besides, the active constraint identification algorithm, and the projection method are changed into less complex approaches. As a result, if a constraint is violated by an agent, a new path will be suggested to correct the direction of the agent's movement. The presented procedure includes three main steps: (1) the identification of the active constraint, (2) the neighboring point determination, and (3) the new direction and step length. Moreover, this method can be applied to some meta-heuristic algorithms. It increases the chance of convergence in the final phase of the search process, especially when the number of the violations of the constraints increases. The method is applied jointly with the authors' newly developed meta-heuristic algorithm, entitled Star Graph. The capability of the resulted hybrid method is examined using the optimal design of truss and frame structures. Eventually, the comparison of the results with other meta-heuristics of the literature shows that the hybrid method is successful in the global as well as local search.

• Industrial Engineering and Management Systems
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• v.11 no.2
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• pp.170-175
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• 2012

In this paper, a procedure using a genetic algorithm (GA) and a heuristic local search (HLS) is proposed for solving facility rearrangement problem (FRP). FRP is a decision problem for stopping/running of facilities and integration of stopped facilities to running facilities to maximize the production capacity of running facilities under the cost constraint. FRP is formulated as an integer programming model for maximizing the total production capacity under the constraint of the total facility operating cost. In the cases of 90 percent of cost constraint and more than 20 facilities, the previous solving method was not effective. To find effective alternatives, this solving procedure using a GA and a HLS is developed. Stopping/running of facilities are searched by GA. The shifting the production operation of stopped facilities into running facilities is searched by HLS, and this local search is executed for one individual in this GA procedure. The effectiveness of the proposed procedure using a GA and HLS is demonstrated by numerical experiment.

• Journal of the Korean Operations Research and Management Science Society
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• v.30 no.2
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• pp.157-167
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• 2005

This paper presents a new perturbation technique for developing efficient iterated local search procedures for the asymmetric traveling salesman problem(ATSP). This perturbation technique uses global information on ATSP instances to speed-up computation and to improve the quality of the tours found by heuristic method. The main idea is to escape from a local optima by introducing perturbations on the out-of-kilter arcs in the problem instance. For a local search heuristic, we use the Kwon which finds optimum or near-optimum solutions by applying the out-of-kilter algorithm to the ATSP. The performance of our algorithm has been tested and compared with known method perturbing on randomly chosen arcs. A number of experiments has been executed both on the well-known TSPLIB instances for which the optimal tour length is known, and on randomly generated Instances. for 27 TSPLIB instances, the presented algorithm has found optimal tours on all instances. And it has effectively found tours near AP lower bound on randomly generated instances.

• The KIPS Transactions:PartB
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• v.15B no.2
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• pp.159-170
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• 2008

Nowadays, one of the important functionalities required from robot task planners is to generate plans to compose existing component services into a new service. In this paper, we introduce the design and implementation of a heuristic search planner, JPLAN, as a kernel module for component service composition. JPLAN uses a local search algorithm and planning graph heuristics. The local search algorithm, EHC+, is an extended version of the Enforced Hill-Climbing(EHC) which have shown high efficiency applied in state-space planners including FF. It requires some amount of additional local search, but it is expected to reduce overall amount of search to arrive at a goal state and get shorter plans. We also present some effective heuristic extraction methods which are necessarily needed for search on a large state-space. The heuristic extraction methods utilize planning graphs that have been first used for plan generation in Graphplan. We introduce some planning graph heuristics and then analyze their effects on plan generation through experiments.

• The Journal of the Korea Contents Association
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• v.10 no.9
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• pp.107-116
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• 2010

The facility location problem is one of the traditional optimization problems. In this paper, we deal with the single source capacitated facility location problem (SSCFLP) and it is known as an NP-hard problem. Thus, it seems to be natural to use a heuristic approach such as evolutionary algorithms for solving the SSCFLP. This paper introduces a new efficient evolutionary algorithm for the SSCFLP. The proposed algorithm is devised by incorporating a general adaptive link adjustment evolutionary algorithm and three heuristic local search methods. Finally we compare the proposed algorithm with the previous algorithms and show the proposed algorithm finds optimum solutions at almost all middle size test instances and very stable solutions at larger size test instances.

• 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 KIISE:Software and Applications
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• v.30 no.9
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• pp.862-866
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• 2003

Traveling Salesman Problem(TSP) is a combinational optimization problem, Genetic Algorithm(GA) and Lin-Kernighan(LK) Heuristic［1］that is Local Search Heuristic are one of the most commonly used methods to resolve TSP. In this paper, we introduce ACS(Ant Colony System) Algorithm as another approach to solve TSP and propose a new pheromone updating method. ACS uses pheromone information between cities in the Process where many ants make a tour, and is a method to find a optimal solution through recursive tour creation process. At the stage of Global Updating of ACS method, it updates pheromone of edges belonging to global best tour of created all edge. But we perform once more pheromone update about created all edges before global updating rule of original ACS is applied. At this process, we use the frequency of occurrence of each edges to update pheromone. We could offer stochastic value by pheromone about each edges, giving all edges' occurrence frequency as weight about Pheromone. This finds an optimal solution faster than existing ACS algorithm and prevent a local optima using more edges in next time search.

• Structural Engineering and Mechanics
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• v.42 no.6
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• pp.783-797
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• 2012

A new hybrid meta-heuristic optimization algorithm is presented for design of structures. The algorithm is based on the concepts of the charged system search (CSS) and the particle swarm optimization (PSO) algorithms. The CSS is inspired by the Coulomb and Gauss's laws of electrostatics in physics, the governing laws of motion from the Newtonian mechanics, and the PSO is based on the swarm intelligence and utilizes the information of the best fitness historically achieved by the particles (local best) and by the best among all the particles (global best). In the new hybrid algorithm, each agent is affected by local and global best positions stored in the charged memory considering the governing laws of electrical physics. Three different types of structures are optimized as the numerical examples with the new algorithm. Comparison of the results of the hybrid algorithm with those of other meta-heuristic algorithms proves the robustness of the new algorithm.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2008.10a
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• pp.131-135
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• 2008

In this paper, we deal with a hierarchical location-allocation problem in designing the broadband convergence networks (BcN). The objective is to minimize the total cost of switch and cable while satisfying the quality of service (QoS). We formulate the problem as an integer programming model and develop the Tabu Search (TS) heuristic algorithm to find a good feasible solution within a reasonable time limit. Initial solution is obtained by using the tree structure. Three neighborhood generation mechanisms are used by local search heuristic: insertion, switch up, and switch down. In order to demonstrate the effectiveness of the proposed algorithm, we generate lower bounds from nonlinear QoS relaxation problem. We present promising computational results of the proposed solution procedures.