کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
382052 660723 2016 19 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Particle Swarm Algorithm variants for the Quadratic Assignment Problems - A probabilistic learning approach
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Particle Swarm Algorithm variants for the Quadratic Assignment Problems - A probabilistic learning approach
چکیده انگلیسی


• A new probability-based approach is proposed for the learning in discrete PSO.
• A generic framework is proposed to discretize PSO and its variants.
• Five well-known PSO variants are discretized based on the proposed framework.
• Comparative evaluation and search landscapes analysis is presented.

The Quadratic Assignment Problem (QAP) has attracted considerable research efforts due to its importance for a number of real life problems, in addition to its acknowledged difficulty. Almost all of the well-known nature-mimicking algorithms have been applied to solve the QAP. However, the Particle Swarm Optimization (PSO), which has proven to be very effective in various applications, has received little attention at this front. The reason can be ascribed to the Euclidian-distance based learning concept (at the core of the algorithm) which makes PSO, in its present form, unsuitable for combinatorial optimization problems. In this article, a new probability-based approach is proposed for the learning in PSO. Based on this learning concept, a generic framework is developed to discretize PSO and its variants, to make them suitable for combinatorial optimization. Five well-known PSO variants are discretized based on this proposed framework. A comparative study of all discretized PSO variants is also included. Moreover, the proposed framework is compared to other attempts to discretize PSO, in addition to three other meta-heuristic approaches. The comparison revealed that the proposed technique is more effective.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Expert Systems with Applications - Volume 44, February 2016, Pages 413–431
نویسندگان
, ,