Teaching-learning-based optimization (TLBO) algorithm, which simulates the process of teaching-learning in the classroom, has been studied by many researchers, and a number of experiments have shown that it has great ...
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Teaching-learning-based optimization (TLBO) algorithm, which simulates the process of teaching-learning in the classroom, has been studied by many researchers, and a number of experiments have shown that it has great performance in solving optimization problems. However, it has an inherent origin bias in teacher phase and may fall into local optima for solving complex high-dimensional optimization problems. Therefore, an improved teaching method is proposed to eliminate the bias of converging toward the origin and enhance the ability of exploration during the convergence process. And a self-learning phase is presented to maintain the ability of exploration after convergence. Besides, a mutation phase is introduced to provide a good mixing ability among the population, preventing premature convergence. As a result, a reformative TLBO (RTLBO) algorithm with three modifications, an improved teaching method, a self-learning phase and a mutation phase, is proposed to significantly improve the performance of the TLBO algorithm. Ten unconstrained benchmark functions and three constrained engineering design problems are employed to evaluate the performance of the RTLBO algorithm. The results of the experiments show that the RTLBO algorithm is of excellent performance and better than, or at least comparable to, other available optimization algorithms in literature.
In this paper, we propose a new population-based framework for combining local search with global explorations to solve single-objective unconstrained numerical optimization problems. The idea is to use knowledge abou...
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ISBN:
(纸本)3540454853
In this paper, we propose a new population-based framework for combining local search with global explorations to solve single-objective unconstrained numerical optimization problems. The idea is to use knowledge about local optima found during the search to a) locate promising regions in the search space and b) identify suitable step sizes to move from one optimum to others in each region. The search knowledge was maintained using a Cultural Algorithm-based structure, which is updated by behaviors of individuals and is used to actively guide the search. Some experiments have been carried out to evaluate the performance of the algorithm on well-known continuous problems. The test results show that the algorithm can get comparable or superior results to that of some current well-known unconstrained numerical optimization algorithms in certain classes of problems.
This thesis addresses the topic of unconstrainedoptimization. It describes seven derivative-free optimization methods for objective functions of multiple variables. Three groups of methods are distinguished. The Alte...
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This thesis addresses the topic of unconstrainedoptimization. It describes seven derivative-free optimization methods for objective functions of multiple variables. Three groups of methods are distinguished. The Alternating Variable method and the method of Hooke and Jeeves represent the pattern search methods. Then there are two simplex algorithms: one by Spendley, Hext and Himsworth and the amoeba algorithm of Nelder and Mead. The family of methods with adaptive sets of search directions consists of Rosenbrocks method, the method of Davies, Swann and Campey, and Powells method. All algorithms are implemented in MATLAB and tested on three functions of two variables. Their progression is illustrated by multiple figures and their comparative analysis is given. Powered by TCPDF (***)
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