All capacities modular cost survivable network design problem using genetic algorithm with completely connection encoding

Bình, Huỳnh Thị Thanh; Ngo, Son Hong

doi:10.1186/s13673-014-0013-y

Cited by 4 publications

(4 citation statements)

References 14 publications

(31 reference statements)

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“…In this study, F7 (level of prices, living costs) and F10 (tourist safety) were found influential factors through fuzzy algorithm analysis [20]. From this research, a fuzzy rule database of tourism destinations is established to provide a fuzzy system inference decision-making model.…”

Section: Resultsmentioning

confidence: 99%

The Development of a Tourism Attraction Model by Using Fuzzy Theory

Chang

2015

Mathematical Problems in Engineering

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The purpose of this study is to develop a model to investigate the tourists’ preference. Ten attributes of tourist destinations were used in this study. Fuzzy set theory was adopted as the main analysis method to find the tourists’ preference. In this study, 248 pieces of data were used. Besides the evaluations for the factors, the overall evaluations (namely, satisfied, neutral, and dissatisfied) for every tourism destination were also inquired. After screening, 201 pieces of these data could be used. In these 201 pieces of data, 141 were classified into “satisfied” with the tourism destination, accounting for 70.15%, and 49 were “neutral,” accounting for 24.38%, while 11 were “dissatisfied,” accounting for 5.47%. Eight rules were obtained with the method of fuzzy preprocess. Regarding the condition attributes, three of the original ten attributes were found influential, namely, level of prices, living costs, information and tourist services, and tourist safety of the tourism destinations. From the results of this study, it is shown that top management of tourism destinations should put resources in these fields first, in order to allow limited resources to perform to maximum effectiveness.

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Section: Resultsmentioning

confidence: 99%

The Development of a Tourism Attraction Model by Using Fuzzy Theory

Chang

2015

Mathematical Problems in Engineering

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“…However, this algorithm is impractical because it does not consider an effective transaction mechanism, including a pricing strategy. In this paper, we propose a strategy to determine the desired sale and purchase prices for the surplus and shortage of electricity, respectively, and an optimization scheme [5][6][7][8][9][10] for finding the most efficient energy route.…”

Section: Introductionmentioning

confidence: 99%

Game-Theory-Based Approach for Energy Routing in a Smart Grid Network

Hong

Kim

2016

Journal of Computer Networks and Communications

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Small power plants and buildings with renewable power generation capability have recently been added to traditional central power plants. Through these facilities, prosumers appear to have a concurrent role in both energy production and consumption. Based on bidirectional power transfers by large numbers of prosumers, a smart microgrid has become an important factor in efficiently controlling the microgrids used in power markets and in conducting effective power trades among grids. In this paper, we present an approach utilizing the game theory for effective and efficient energy routing, which is a novel and challenging procedure for a smart microgrid network. First, we propose strategies for choosing the desired transaction price for both electricity surpluses and shortages to maximize profits through energy transactions. An optimization scheme is utilized to search for an energy route with minimum cost using the solving method used in a traditional transportation problem by treating the sale and purchase quantities as transportation supply and demand, respectively. To evaluate the effect of the proposed decision strategies, we simulated our mechanism, and the results proved that our mechanism yields results pursued by each strategy. Our proposed strategies will contribute to spreading a smart microgrid for enhancing the utilization of microgrids.

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“…Metaheuristic algorithms are widely used to solve problems of optimization, such as schedule management [14,15], function optimization [16], and intrusion detection [17]. Metaheuristic algorithms combine random search functions with empirical rules, and many of these methods have been inspired by mechanisms found in nature, such as genetic algorithms (GA) [18][19][20], based on gene mutation and mating, and particle swarm optimization (PSO) [21], based on the movements of flocks of birds. In 2002, the artificial fish swarm algorithm (AFSA) [22] was proposed to solve problems of optimality by simulating the movement of schools of fish and the intelligence underlying these behaviors.…”

Section: Introductionmentioning

confidence: 99%

Feature Selection and Parameter Optimization of Support Vector Machines Based on Modified Artificial Fish Swarm Algorithms

Lin

Chen

Hung

2015

Mathematical Problems in Engineering

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Rapid advances in information and communication technology have made ubiquitous computing and the Internet of Things popular and practicable. These applications create enormous volumes of data, which are available for analysis and classification as an aid to decision-making. Among the classification methods used to deal with big data, feature selection has proven particularly effective. One common approach involves searching through a subset of the features that are the most relevant to the topic or represent the most accurate description of the dataset. Unfortunately, searching through this kind of subset is a combinatorial problem that can be very time consuming. Meaheuristic algorithms are commonly used to facilitate the selection of features. The artificial fish swarm algorithm (AFSA) employs the intelligence underlying fish swarming behavior as a means to overcome optimization of combinatorial problems. AFSA has proven highly successful in a diversity of applications; however, there remain shortcomings, such as the likelihood of falling into a local optimum and a lack of multiplicity. This study proposes a modified AFSA (MAFSA) to improve feature selection and parameter optimization for support vector machine classifiers. Experiment results demonstrate the superiority of MAFSA in classification accuracy using subsets with fewer features for given UCI datasets, compared to the original FASA.

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All capacities modular cost survivable network design problem using genetic algorithm with completely connection encoding

Cited by 4 publications

References 14 publications

The Development of a Tourism Attraction Model by Using Fuzzy Theory

The Development of a Tourism Attraction Model by Using Fuzzy Theory

Game-Theory-Based Approach for Energy Routing in a Smart Grid Network

Feature Selection and Parameter Optimization of Support Vector Machines Based on Modified Artificial Fish Swarm Algorithms

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