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首页> 外文期刊>International journal of systems assurance engineering and management >A new ants interaction scheme for continuous optimization problems
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A new ants interaction scheme for continuous optimization problems

机译：求解连续优化问题的新蚂蚁交互方案

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摘要

Ant colony optimization (ACO) algorithms have been used successfully to solve a wide variety of combinatorial optimization problems. In the recent past many modifications have been proposed in ACO algorithms to solve continuous optimization problems. However, most of the ACO variants to solve continuous optimization problems lack ability of efficient exploration of the search space and suffer from the problem of premature convergence. In this work a new ACO algorithm (ACO–LD) is proposed that incorporates Laplace distribution based interaction scheme among the ants. Also, in order to avoid the problem of stagnation, an additional diversification mechanism is introduced. The proposed ACO–LD is tested on benchmark test functions taken from Congress on Evolutionary Computation 2014 (CEC2014) and the results are compared with four state-of-the-art algorithms reported in CEC2014. ACO–LD is also applied to solve six real life problems and the results are compared with the results of six other algorithms reported in the literature. The analysis of the results shows that the overall performance of ACO–LD is found to be better than the other algorithms included in the present study.
机译：蚁群优化（ACO）算法已成功用于解决各种各样的组合优化问题。在最近的过去，已经提出了对ACO算法的许多修改以解决连续优化问题。但是，大多数解决连续优化问题的ACO变体缺乏有效探索搜索空间的能力，并且存在过早收敛的问题。在这项工作中，提出了一种新的ACO算法（ACO-LD），该算法结合了基于拉普拉斯分布的蚂蚁之间的交互方案。另外，为了避免停滞的问题，引入了附加的多样化机制。拟议中的ACO-LD在国会进化计算2014（CEC2014）的基准测试功能上进行了测试，并将结果与​​CEC2014中报告的四种最新算法进行了比较。 ACO-LD还用于解决六个现实生活中的问题，并将结果与​​文献中报道的其他六个算法的结果进行了比较。结果分析表明，发现ACO-LD的总体性能优于本研究中包括的其他算法。

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