Improve self adptive control prameters in differential evolution algorithm for complex numerical optimization problems
➤ Gửi thông báo lỗi ⚠️ Báo cáo tài liệu vi phạmNội dung chi tiết: Improve self adptive control prameters in differential evolution algorithm for complex numerical optimization problems
Improve self adptive control prameters in differential evolution algorithm for complex numerical optimization problems
Improve Self-Adaptive Control Parameters ill Differential Evolution Algorithm for Complex Numerical Optimization ProblemsA DISSERTATION SUBMITTED TO G Improve self adptive control prameters in differential evolution algorithm for complex numerical optimization problems GRADUATE SCHOOL OF ENGINEERING AND SCIENCE OF SHIBAURA INSTI TUTE OF TECHNOLOGYbyBUI NGOC TAMIN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OFDOCTOR OF ENGINEERING42248AcknowledgmentsThis dissertation is a result of research that has been performed at the Hasegawa laboratory. College of S Improve self adptive control prameters in differential evolution algorithm for complex numerical optimization problems ystems Engineering and Science, Shibaura Institute of Technology, .Japan, under the supervision of Prof. Hiroshi Hasegawa. Completion of this doctoralImprove self adptive control prameters in differential evolution algorithm for complex numerical optimization problems
dissertation was possible with the support of several people. I would like to express my sincere gratitude to all of them.First of all. I am heartilyImprove Self-Adaptive Control Parameters ill Differential Evolution Algorithm for Complex Numerical Optimization ProblemsA DISSERTATION SUBMITTED TO G Improve self adptive control prameters in differential evolution algorithm for complex numerical optimization problems an understanding of the subject. 1 am sure it would have not been possible without his help.1 would like to acknowledge the financial, academic and technical support, gradate school section and Student Affairs Section especially Ms.Yabe in Omiya campus of the Shibaura Institute of Technology.I woul Improve self adptive control prameters in differential evolution algorithm for complex numerical optimization problems d like to thank all other members of Hasegawa laboratory for their contributions to all kinds of discussions on various topics, and their support withImprove self adptive control prameters in differential evolution algorithm for complex numerical optimization problems
respect . The group has been a source of friendships as well as good advice and collaborationI would like to thank to my wife Nguyen Thi I lien for hImprove Self-Adaptive Control Parameters ill Differential Evolution Algorithm for Complex Numerical Optimization ProblemsA DISSERTATION SUBMITTED TO G Improve self adptive control prameters in differential evolution algorithm for complex numerical optimization problems as always, for which my mere expression of I hanks hkewise does nor. suffice.■Japan, September 2015BUI NGOC TAMAbstractMemetic Algorithms (MA) is effective algorithms to obtain reliable and accurate solutions for complex continuous optimization problems. Nowadays, high dimensional optimization prob Improve self adptive control prameters in differential evolution algorithm for complex numerical optimization problems lems arc an interesting field of research. To solve complex numerical optimization problems, researchers have been looking into nature both as model aImprove self adptive control prameters in differential evolution algorithm for complex numerical optimization problems
nd as metaphor for inspiration. A keen observation of the underlying relation between optimization and biological evolution led to the development of Improve Self-Adaptive Control Parameters ill Differential Evolution Algorithm for Complex Numerical Optimization ProblemsA DISSERTATION SUBMITTED TO G Improve self adptive control prameters in differential evolution algorithm for complex numerical optimization problems s, such as growth or development in a population that is then selected in a guided random search using parallel processing to achieve the desired end. Nowadays, the field of nature-inspired metaheuristics is mostly continued by the Evolution Algorithms (EAs) (c.g., Genetic Algorithms (GAs), Evolutio Improve self adptive control prameters in differential evolution algorithm for complex numerical optimization problems n Strategies (ESs), and Differential Evolution (DE) etc.) as well as the Swarm Intelligence algorithms (e.g., Ant Colony Optimization (ACO), ParticleImprove self adptive control prameters in differential evolution algorithm for complex numerical optimization problems
Swarm Optimization (PSO), Artificial Bee Colony (ABC), etc.). Also the field extends in a broader sense to include selforganizing systems, artificial Improve Self-Adaptive Control Parameters ill Differential Evolution Algorithm for Complex Numerical Optimization ProblemsA DISSERTATION SUBMITTED TO G Improve self adptive control prameters in differential evolution algorithm for complex numerical optimization problems ment self-adaptive for controlling parameters in differential evolution (1SADE) and investigate the hybridization of a local search algorithm wit h an evolution algorithm (H-MNSJSADE), which are the Nelder-Mead simplex method (MNS) and differential evolution (DE), for Complex numerical optimizationp Improve self adptive control prameters in differential evolution algorithm for complex numerical optimization problems roblems. Tills approach hybrid integrate differential evolution with Nelder-Mead simplex method technique is a component based on whore rhe DE algoritImprove self adptive control prameters in differential evolution algorithm for complex numerical optimization problems
hm is integrated with the principle of Nclder-Mead simplex method to improve the neighborhood search of the each particle in H-MN'S ISA DE. By using lImprove Self-Adaptive Control Parameters ill Differential Evolution Algorithm for Complex Numerical Optimization ProblemsA DISSERTATION SUBMITTED TO G Improve self adptive control prameters in differential evolution algorithm for complex numerical optimization problems idanced. All rhe algorithms applied to the some benchmark functions and compared based on some different metrics.This dissertation includes three main points - firstly, we propose the improvement self-adaptive for controlling parameters in differential evolution (1SADE) to solve large scale optimiza Improve self adptive control prameters in differential evolution algorithm for complex numerical optimization problems tion problems, to reduce calculation cost, and to improve stability of convergence towards the optimal solution; secondly, new algorithms (ISADE) is aImprove self adptive control prameters in differential evolution algorithm for complex numerical optimization problems
pplied to several numerical benchmark tests, constrained real parameter optimization and trained artificial neural network to evaluate its perfor-maneImprove Self-Adaptive Control Parameters ill Differential Evolution Algorithm for Complex Numerical Optimization ProblemsA DISSERTATION SUBMITTED TO G Improve self adptive control prameters in differential evolution algorithm for complex numerical optimization problems x method (MNS) and differential evolution (DE):ContentsAbstractiiiList of FiguresXList of TablesxiList of Algorithmxii1Introduction11.1Optimal Systems Design..................................... 11.2Optimal Design of Complex Mechanical Systems............... 21.3Constraints and Challenges .......... Improve self adptive control prameters in differential evolution algorithm for complex numerical optimization problems ...................... 81.3.1Method of Lagrange Multipliers...................... 81.3.2Penalty Method..................................... 121.3.3SteImprove self adptive control prameters in differential evolution algorithm for complex numerical optimization problems
p Size in Random Walks.......................... 131.4Motivation and Objects ................................... 11Improve Self-Adaptive Control Parameters ill Differential Evolution Algorithm for Complex Numerical Optimization ProblemsA DISSERTATION SUBMITTED TO GImprove Self-Adaptive Control Parameters ill Differential Evolution Algorithm for Complex Numerical Optimization ProblemsA DISSERTATION SUBMITTED TO GGọi ngay
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