The Continuous Differential Ant-Stigmergy Algorithm applied on real-parameter single objective optimization problems

2013 IEEE Congress on Evolutionary Computation Pub Date : 2013-06-20 DOI:10.1109/CEC.2013.6557760

P. Korošec, J. Silc

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引用次数: 16

Abstract

Continuous ant-colony optimization is an emerging field in numerical optimization, which tries to cope with the optimization challenges arising in modern real-world engineering and scientific domains. One of them is large-scale continuous optimization problem that becomes especially important for the development of recent emerging fields like bio-computing, data mining and production planing. Ant-colony optimization (ACO) is known for its efficiency in solving combinatorial optimization problems. However, its application to real-parameter optimizations appears more challenging, since the pheromone-laying method is not straightforward. In the recent year, there have been developed a several adaptations of the ACO algorithm for continuous optimization. Among them the Continuous Differential Ant-Stigmergy Algorithm (CDASA) arises as promising method for global continuous large-scale optimization. In this paper we address a systematic performance evaluation of CDASA on a predefined test suite and experimental procedure provided for the Competition on Real-Parameter Single Objective Optimization at CEC-2013.

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连续微分反stigmergy算法在实参数单目标优化问题中的应用

连续蚁群优化是数值优化中的一个新兴领域，它试图应对现代现实工程和科学领域中出现的优化挑战。其中之一是大规模连续优化问题，这对生物计算、数据挖掘和生产计划等新兴领域的发展尤为重要。蚁群算法以其解决组合优化问题的效率而闻名。然而，将其应用于实际参数优化似乎更具挑战性，因为信息素铺设方法并不简单。近年来，针对蚁群算法的连续优化问题，出现了几种改进的蚁群算法。其中，连续微分反stigmergy算法(CDASA)是一种很有前途的全局连续大规模优化方法。本文在CEC-2013实参数单目标优化竞赛中提供的预定义测试套件和实验程序上对CDASA进行了系统的性能评估。

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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2013 IEEE Congress on Evolutionary Computation

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