In this study, a new stopping criterion, called "backward controlled stopping criterion" (BCSC), was proposed to be used in geneticalgorithms. In the study, the available stopping citeria;adaptive stopping ...
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In this study, a new stopping criterion, called "backward controlled stopping criterion" (BCSC), was proposed to be used in geneticalgorithms. In the study, the available stopping citeria;adaptive stopping citerion, evolution time, fitness threshold, fitness convergence, population convergence, gene convergence, and developed stopping criterion were applied to the following four comparison problems;high strength concrete mix design, pre-stressed precast concrete beam, travelling salesman and reinforced concrete deep beam problems. When completed the analysis, the developed stopping criterion was found to be more accomplished than available criteria, and was able to research a much larger area in the space design supplying higher fitness values.
In this article we demonstrate the supremacy of the Non-dominated Sorting geneticalgorithm-II with Simulated Binary Crossover and Polynomial Mutation operators for the multi-objective optimization of Stirling engine ...
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In this article we demonstrate the supremacy of the Non-dominated Sorting geneticalgorithm-II with Simulated Binary Crossover and Polynomial Mutation operators for the multi-objective optimization of Stirling engine systems by providing three examples, viz., (i) finite time thermodynamic model, (ii) Stirling engine thermal model with associated irreversibility and (iii) polytropic finite speed based thermodynamics. The finite time thermodynamic model involves seven decision variables and consists of three objectives: output power, thermal efficiency and rate of entropy generation. In comparison to literature, it was observed that the used strategy provides a better Pareto front and leads to improvements of up to 29%. The performance is also evaluated on a Stirling engine thermal model which considers the associated irreversibility of the cycle and consists of three objectives involving eleven decision variables. The supremacy of the suggested strategy is also demonstrated on the experimentally validated polytropic finite speed thermodynamics based Stirling engine model for optimization involving two objectives and ten decision variables. (C) 2016 Elsevier Ltd. All rights reserved.
This study presents a fuzzy logic-based strategy to solve thermal generation scheduling [namely unit commitment (UC)] problem integrated with an equivalent solar-battery system using quantum inspired evolutionary algo...
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This study presents a fuzzy logic-based strategy to solve thermal generation scheduling [namely unit commitment (UC)] problem integrated with an equivalent solar-battery system using quantum inspired evolutionary algorithm. Solar-battery system is included with this model as a measure of green-house effect. The crisp formulations of UC are modified utilising fuzzy logics, because of the inherent intermittency involved in solar energy integration and other uncertain variables. An evolutionary algorithm based on the concept and principle of quantum computation is applied to solve the resultant fuzzy logic-based UC problem. Conventional quantum evolutionary algorithm (QEA) is modified by incorporating a hierarchy-group oriented scheme to deal with the non-linear and multi-peak nature of the problem. QEA is further advanced facilitating some genetic algorithm operators and a new binary differential operator along with rotation operator with a redefined rotational angle look-up table. The probabilities of using such operators on individual solution(s) are fuzzified by defining membership function based on associated fitness of that individual. The fitness function is then determined by combining the objective function, penalty function and the aggregated fuzzy membership function. The proposed fuzzy logic-based QEA (FLQEA) is applied to UC problem in two different scaled power systems. Provided simulation results will show the effectiveness of FLQEA.
in this paper, we have proposed a novel use of data mining algorithms for the extraction of knowledge from a large set of flow shop schedules. The purposes of this work is to apply data mining methodologies to explore...
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in this paper, we have proposed a novel use of data mining algorithms for the extraction of knowledge from a large set of flow shop schedules. The purposes of this work is to apply data mining methodologies to explore the patterns in data generated by an ant colony algorithm performing a scheduling operation and to develop a rule set scheduler which approximates the ant colony algorithm's scheduler. Ant colony optimization (ACO) is a paradigm for designing metaheuristic algorithms for combinatorial optimization problems. The natural metaphor on which ant algorithms are based is that of ant colonies. Fascinated by the ability of the almost blind ants to establish the shortest route from their nests to the food source and back, researchers found out that these ants secrete a substance called pheromone' and use its trails as a medium for communicating information among each other. The ant algorithm is simple to implement and results of the case studies show its ability to provide speedy and accurate solutions. Further, we employed the genetic algorithm operators such as crossover and mutation to generate the new regions of solution. The data mining tool we have used is Decision Tree, which is produced by the See5 software after the instances are classified. The data mining is for mining the knowledge of job scheduling about the objective of minimization of makespan in a flow shop environment. Data mining systems typically uses conditional relationships represented by IF-THEN rules and allowing the production managers to easily take the decisions regarding the flow shop scheduling based on various objective functions and the constraints. (C) 2009 Elsevier Ltd. All rights reserved.
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