A Teaching-Learning-Based Optimization with Uniform Design for Solving Constrained Optimization Problems

2017 13th International Conference on Computational Intelligence and Security (CIS) Pub Date : 2017-12-01 DOI:10.1109/CIS.2017.00058

Liping Jia, Zhonghua Li

引用次数: 3

Abstract

As a newly developed population-based metaheuristic algorithm, teaching-learning-based optimization (TBLO) has been gained extensively attention since it was proposed in 2011. It has been applied to many optimal problems and a lot of algorithms have also been designed to solve these real-world problems. In this paper, TBLO with uniform design is proposed for solving constrained optimization problems. The performance of the proposed algorithm is checked by experiments with two type of different benchmark problems under the criteria of best, mean, worst, function evaluations and ratio of feasible search space. Compared results are given to illustrate the efficiency of the proposed algorithm.

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基于教-学的统一设计优化求解约束优化问题

基于教学的优化算法(teaching-learning-based optimization, TBLO)作为一种新兴的基于群体的元启发式算法，自2011年提出以来受到了广泛关注。它已经应用于许多最优问题，许多算法也被设计来解决这些现实世界的问题。本文提出了一种均匀设计的TBLO来求解约束优化问题。在最佳、平均、最差、函数评价和可行搜索空间比等标准下，对两类不同的基准问题进行了实验，验证了算法的性能。比较结果说明了所提算法的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2017 13th International Conference on Computational Intelligence and Security (CIS)

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