کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4946851 1439557 2017 23 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Multi-objective dynamic economic emission dispatch using particle swarm optimisation variants
ترجمه فارسی عنوان
اعزام نیروی اقتصادی پویا چند هدفه با استفاده از انواع بهینه سازی ذرات
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی
Particle swarm optimisation (PSO) is a bio-inspired swarm based approach to solving optimisation problems. The algorithm functions as a result of particles traversing and evaluating the problem space, eventually converging on the optimum solution. This paper applies a number of PSO variants to the dynamic economic emission dispatch (DEED) problem. The DEED problem is a multi-objective optimisation problem in which the goal is to optimise two conflicting objectives: cost and emissions. The PSO variants tested include: the standard PSO (SPSO), the PSO with avoidance of worst locations (PSO AWL), and also a selection of different topologies including the PSO with a gradually increasing directed neighbourhood (PSO GIDN). The aim of the paper is to test the performance of different variants of the PSO AWL against variants of the SPSO on the DEED problem. The results show that the PSO AWL outperforms the SPSO for every topology implemented. The results are also compared to state of the art genetic algorithm (NSGA-II) and multi-agent eeinforcement learning (MARL). This paper then examines the performance of each PSO algorithm when the power demand is modified to form a triangle wave. The purpose of this experiment was to analyse the performance of different PSO variants on an increasingly constrained problem.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 270, 27 December 2017, Pages 188-197
نویسندگان
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