In the traditional scheduling mode of the current thermal power plants,only the power load distribution of the unit was *** there was no enough guidance to the heat source *** can not meet the current energy-saving an...
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ISBN:
(纸本)9781509046584
In the traditional scheduling mode of the current thermal power plants,only the power load distribution of the unit was *** there was no enough guidance to the heat source *** can not meet the current energy-saving and emission-reduction *** this paper,an optimal heat load dispatching model is set up under the premise of power load distribution of thermal power *** kinds of heat loads including to high-pressure,medium-pressure and low-pressure are taken into account in the scheduling *** to the characteristics of the problem,brainstormingoptimization(BSO) algorithm is used to get the optimal distribution *** comparison with the original scheme shows the correctness of the model and the optimization ***,the optimization ability and effectiveness of brain storming optimization algorithm in solving complex optimization problems are verified by comparing the proposed algorithm with the other different optimizationalgorithms,such as Particle Swarm optimization(PSO) and Differential Evolution(DE) algorithm.
The brainstormingoptimization (BSO) algorithm is a novel swarm intelligent algorithm that simulates the brainstorming process of humans. This paper presents the BSO algorithm as a solution to the Flexible Job-Shop S...
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The brainstormingoptimization (BSO) algorithm is a novel swarm intelligent algorithm that simulates the brainstorming process of humans. This paper presents the BSO algorithm as a solution to the Flexible Job-Shop Scheduling Problem (FJSSP). In aim to improve the global search of the BSO algorithm, a new updating strategy is proposed to adaptively perform several selection methods and neighborhood structures. Furthermore, BSO algorithm has good ability in exploring the search space by clustering the solutions and searching in each cluster independently, thus leading to slow convergence speed, to enhance the local intensification capability and to overcome the slow convergence of the BSO algorithm, we introduce Late Acceptance Hill Climbing (LAHC) with three neighborhoods to the BSO algorithm. Extensive computational experiments were carried out on four well-known benchmarks for FJSSP, and the performance of the BSO algorithm was compared with that of the proposed algorithm. The results demonstrate that the proposed algorithm outperforms the BSO algorithm. Furthermore, the proposed approach overcomes the best-known algorithms in some datasets and it is comparable with these algorithms in other datasets. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of King Saud University.
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