parametersettings for nature-inspired optimization algorithms are essential for their effective performance. Evolutionary algorithms and swarm intelligence algorithms are prominent types of nature-inspired optimizati...
详细信息
parametersettings for nature-inspired optimization algorithms are essential for their effective performance. Evolutionary algorithms and swarm intelligence algorithms are prominent types of nature-inspired optimization. There are comprehensive reviews of parametersetting techniques for evolutionary algorithms. Counterparts providing an overview of parametersetting techniques for swarm intelligence algorithms are needed also. Therefore, in this paper, we provide a critical and comprehensive review, focusing in particular on dynamic parameter setting techniques. The paper describes a variety of swarm intelligence algorithms and parametersetting approaches that have been applied to them. This review simplifies the selection of parametersetting techniques for each algorithm by collecting them in a single document and classifying them under a taxonomy. Recommendations for parametersetting approach selection are provided in this review. We explore the open problems related to dynamic parameter setting techniques for swarm intelligence optimization and discuss the trade-off between run-time computation and flexibility of these algorithms.
The use of meta-heuristics is very common when solving a combinatorial problem in practice. Some approaches provide very good quality solutions in a short amount of computational time, however the parameters must be s...
详细信息
ISBN:
(纸本)9789806560710
The use of meta-heuristics is very common when solving a combinatorial problem in practice. Some approaches provide very good quality solutions in a short amount of computational time, however the parameters must be set before solving the problem which could require much time. This paper investigates the problem of settingparameters using a typical meta-heuristic called Meta-RaPS (Metaheuristic for Randomized Priority Search.). Meta-RaPS is a promising meta-heuristic optimization method that has been applied to different types of combinatorial optimization problems and achieved very good performance compared to other meta-heuristic techniques. To solve a problem, Meta-RaPS uses two well-defined stages at each iteration: construction and local search. After a number of / iterations, the best solution is reported. Meta-RaPS performance depends on the fine tuning of two parameters: the priority percentage and restriction percentage, which are used during the construction stage. This paper presents two different dynamic parameter setting methods to set Meta-RaPS parameters while at the same time a solution is being found. To compare these two approaches, nonparametric statistic approaches are utilized since the distribution of solutions is not normal. Results from both these dynamic parameter setting methods are reported.
We present an new optimisation framework combining two metaheuristics: Genetic Algorithms (GA) and Particle Swarm Optimisation (PSO). In contrast to the usual hybridisation models in which the second algorithm is appl...
详细信息
ISBN:
(纸本)9781450349390
We present an new optimisation framework combining two metaheuristics: Genetic Algorithms (GA) and Particle Swarm Optimisation (PSO). In contrast to the usual hybridisation models in which the second algorithm is applied to work on the final results of the first one, our approach uses both algorithms in parallel on the same population in a competetive manner. The algorithms can work on and improve the solutions of each other, thus more diversity and better quality can be achieved in the population. Another improving factor is resetting the population size and the parameters in every iteration according to the diversity and quality of the solutions in the last population. Our approach is tested on five well-known benchmark problems. The merit of our approach is verified by comparing its performance with the pure GA and PSO, hybrids where PSO works after GA, and vice versa, as well as another hybrid approach of these algorithms from the literature.
parameter identification problem will be presented, and solved through our new real-coded genetic Algorithm. The algorithm is a modified version from normal GA but it includes biased initialization, dynamicparameters...
详细信息
parameter identification problem will be presented, and solved through our new real-coded genetic Algorithm. The algorithm is a modified version from normal GA but it includes biased initialization, dynamicparameters, and elitism. The algorithm will be tested on three cases. (c) 2006 Elsevier Inc. All rights reserved.
This paper describes a new dynamic evolutionary mechanism which assists process engineers in devising efficient processes for manufacturing high quality items where the mixed production approach is adopted. An adaptiv...
详细信息
This paper describes a new dynamic evolutionary mechanism which assists process engineers in devising efficient processes for manufacturing high quality items where the mixed production approach is adopted. An adaptive system, including the use of genetic algorithms (GA) as a dynamic searching mechanism, is designed in order to maximize the stability of the quality control in the mixed production processes. GA is an effective approach in optimization as it is able to alter manufacturing variables so as to reach a global optimum in complex production processes such as multiple quality chains. The choice of the GA operators and its parameters, however, is a significant problem and inappropriate selection of chromosome structure can lead to poor performance. In order to deal with these issues, a dynamicparameter and operator setting approach with a mechanism based on quality control chart theory, is proposed. The approach allows a trade-off between exploration and exploitation processes in the search. The mechanism applies evolution evidence to supervise and adjust the GA parametersettings at run time. A prototype system has been implemented and applied to optimization problems in multiple quality chains. The experimental results have revealed that the dynamicsetting approach can improve the performance of a GA process in multiple quality chains. The results also established that the dynamicsetting approach is superior to a static one.
Genetic Algorithm (GA) technique is a promising approach to identify an optimum manufacturing cost by concurrently considering significant number of related variables. It is a superior technique for discovering optima...
详细信息
ISBN:
(纸本)142440164X
Genetic Algorithm (GA) technique is a promising approach to identify an optimum manufacturing cost by concurrently considering significant number of related variables. It is a superior technique for discovering optimal alternative solutions for multiple objective optimization problems. In GA, however, the setting of operators and the selections of their parameters could affect the output significantly, as they influence the processes of exploitation and exploration. The function of the dynamicsetting is to adjust the trade-off between the exploration and exploitation of the search space. This research focuses on the application of evolution evidence to supervise the dynamicsetting of the GA parameters. Theoretical test-beds were carried out to verify the theory. The practical experiments were conducted to solve the optimization problems in supply chain management. The experimental results have revealed that the dynamicsetting improves the performance of supply chain management and the results also disclosed that dynamic is superior to static setting.
This paper describes a new dynamic evolutionary mechanism which assists process engineers in devising efficient processes for manufacturing high quality items where the mixed production approach is adopted. An adaptiv...
详细信息
This paper describes a new dynamic evolutionary mechanism which assists process engineers in devising efficient processes for manufacturing high quality items where the mixed production approach is adopted. An adaptive system, including the use of genetic algorithms (GA) as a dynamic searching mechanism, is designed in order to maximize the stability of the quality control in the mixed production processes. GA is an effective approach in optimization as it is able to alter manufacturing variables so as to reach a global optimum in complex production processes such as multiple quality chains. The choice of the GA operators and its parameters, however, is a significant problem and inappropriate selection of chromosome structure can lead to poor performance. In order to deal with these issues, a dynamicparameter and operator setting approach with a mechanism based on quality control chart theory, is proposed. The approach allows a trade-off between exploration and exploitation processes in the search. The mechanism applies evolution evidence to supervise and adjust the GA parametersettings at run time. A prototype system has been implemented and applied to optimization problems in multiple quality chains. The experimental results have revealed that the dynamicsetting approach can improve the performance of a GA process in multiple quality chains. The results also established that the dynamicsetting approach is superior to a static one.
暂无评论