Adaptive Scatter Search to Solve the Minimum Connected Dominating Set Problem for Efficient Management of Wireless Networks

An efficient routing using a virtual backbone (VB) network is one of the most significant improvements in the wireless sensor network (WSN). One promising method for selecting this subset of network nodes is by finding the minimum connected dominating set (MCDS), where the searching space for finding a route is restricted to nodes in this MCDS. Thus, finding MCDS in a WSN provides a flexible low-cost solution for the problem of event monitoring, particularly in places with limited or dangerous access to humans as is the case for most WSN deployments. In this paper, we proposed an adaptive scatter search (ASS-MCDS) algorithm that finds the near-optimal solution to this problem. The proposed method invokes a composite fitness function that aims to maximize the solution coverness and connectivity and minimize its cardinality. Moreover, the ASS-MCDS methods modified the scatter search framework through new local search and solution update procedures that maintain the search objectives. We tested the performance of our proposed algorithm using different benchmark-test-graph sets available in the literature. Experiments results show that our proposed algorithm gave good results in terms of solution quality

Studies on Metaheuristics for Continuous Global Optimization Problems

The interface between computer science and operations research has drawn much attention recently especially in optimization which is a main tool in operations research. In optimiza-tion area, the interest on this interface has rapidly increased in the last few years in order to develop nonstandard algorithms that can deal with optimization problems which the stan-dard optimization techniques often fail to deal with. Global optimization problems represent a main category of such problems. Global optimization refers to finding the extreme value of a given nonconvex function in a certain feasible region and such problems are classified in two classes; unconstrained and constrained problems. Solving global optimization prob-lems has made great gain from the interest in the interface between computer science and operations research. In general, the classical optimization techniques have difficulties in dealing with global optimization problems. One of the main reasons of their failure is that they can easily be entrapped in local minima. Moreover, these techniques cannot generate or even use the global information needed to find the global minimum for a function with multiple local minima. The interaction between computer science and optimization has yielded new practical solver

レンゾクガタ タイイキテキ サイテキカ モンダイ ニ タイスル メタヒューリスティクス ニ カンスル ケンキュウ

京都大学0048新制・課程博士博士(情報学)甲第11155号情博第130号新制||情||30(附属図書館)22724UT51-2004-R30京都大学大学院情報学研究科数理工学専攻(主査)教授 福嶋 雅夫, 教授 片山 徹, 教授 酒井 英昭学位規則第4条第1項該当Doctor of InformaticsKyoto UniversityDA

Derivative-Free Filter Simulated Annealing Method for Constrained Continuous Global Optimization

In this paper, a simulated-annealing-based method called Filter Simulated Annealing (FSA) method is proposed to deal with the constrained global optimization problem. The considered problem is reformulated so as to take the form of optimizing two functions; the objective function and the constraint violation function. Then, the FSA method is applied to solve the reformulated problem. The FSA method invokes a multi-start diversification scheme in order to achieve an e#cient exploration process

Tabu Search directed by direct search methods for Nonlinear Global Optimization

In recent years, there has been a great deal of interest in metaheuristics in the optimization community. Tabu Search (TS) represents a popular class of metaheuristics. However, compared with other metaheuristics like genetic algorithm and simulated annealing, contributions of TS that deals with continuous problems are still very limited. In this paper, we introduce a continuous TS called Directed Tabu Search (DTS) method. In the DTS method, direct-search-based strategies are used to direct a tabu search. These strategies are based on the well-known Nelder-Mead method and a new pattern search procedure called adaptive pattern search. Moreover, we introduce a new tabu list conception with anti-cycling rules called Tabu Regions and Semi-Tabu Regions. In addition, Diversification and Intensification search schemes are employed. Numerical results show that the proposed method is promising and produces high quality solutions

Memetic Algorithm with Filtering Scheme for the Minimum Weighted Edge Dominating Set Problem

The minimum weighted edge dominating set problem (MWEDS) generalizes both the weighted vertex cover problem and the problem of covering the edges of graph by a minimum cost set of both vertices and edges. In this paper, we propose a meta-heuristic approach based on genetic algorithm and local search to solve the MWEDS problem. Therefore, the proposed method is considered as a memetic search algorithm which is called Memetic Algorithm with filtering scheme for minimum weighted edge dominating set, and called shortly (MAFS). In the MAFS method, three new fitness functions are invoked to effectively measure the solution qualities. The search process in the proposed method uses intensification scheme, called “filtering”, beside the main genetic search operations in order to achieve faster performance. The experimental results proves that the proposed method is promising in solving the MWEDS problem

Memetic Algorithm for the Minimum Edge Dominating Set Problem

The minimum edge dominating set (MEDS) is one of the fundamental covering problems in graph theory, which finds many practical applications in diverse domains. In this paper, we propose a meta-heuristic approach based on genetic algorithm and local search to solve the MEDS problem. Therefore, the proposed method is considered as a memetic search algorithm which is called Memetic Algorithm for minimum edge dominating set (MAMEDS). In the MAMEDS method, a new fitness function is invoked to effectively measure the solution qualities. The search process in the proposed method uses intensification schemes beside the main genetic search operations in order to achieve faster performance. The experimental results proves that the proposed method is promising in solving the MEDS problem