Neighborhood-based differential evolution algorithm with direction induced strategy for the large-scale combined heat and power economic dispatch problem

Liu, Di; Hu, Zhongbo; Su, Qinghua

doi:10.1016/j.ins.2022.09.025

Cited by 16 publications

(2 citation statements)

References 41 publications

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“…The information exchange is helpful to escape from local optima during the evolutionary process. Liu et al [32] considered the economic dispatch problem by incorporating a direction-inducted strategy in neighborhood-based DE algorithm in their research work. They have used a new mutation strategy named a neighborhood-based nonelite direction strategy that enhances the exploitation capability of the presented algorithm.…”

Section: Use Of Neighbor Informationmentioning

confidence: 99%

Differential Evolution Using Enhanced Mutation Strategy Based on Random Neighbor Selection

Baig,

Abbas,

Ahmad

et al. 2023

Symmetry

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Symmetry in a differential evolution (DE) transforms a solution without impacting the family of solutions. For symmetrical problems in differential equations, DE is a strong evolutionary algorithm that provides a powerful solution to resolve global optimization problems. DE/best/1 and DE/rand/1 are the two most commonly used mutation strategies in DE. The former provides better exploitation while the latter ensures better exploration. DE/Neighbor/1 is an improved form of DE/rand/1 to maintain a balance between exploration and exploitation which was used with a random neighbor-based differential evolution (RNDE) algorithm. However, this mutation strategy slows down convergence. It should achieve a global minimum by using 1000 × D, where D is the dimension, but due to exploration and exploitation balancing trade-offs, it can not achieve a global minimum within the range of 1000 × D in some of the objective functions. To overcome this issue, a new and enhanced mutation strategy and algorithm have been introduced in this paper, called DE/Neighbor/2, as well as an improved random neighbor-based differential evolution algorithm. The new DE/Neighbor/2 mutation strategy also uses neighbor information such as DE/Neighbor/1; however, in addition, we add weighted differences after various tests. The DE/Neighbor/2 and IRNDE algorithm has also been tested on the same 27 commonly used benchmark functions on which the DE/neighbor/1 mutation strategy and RNDE were tested. Experimental results demonstrate that the DE/Neighbor/2 mutation strategy and IRNDE algorithm show overall better and faster convergence than the DE/Neighbor/1 mutation strategy and RNDE algorithm. The parametric significance test shows that there is a significance difference in the performance of RNDE and IRNDE algorithms at the 0.05 level of significance.

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Section: Use Of Neighbor Informationmentioning

confidence: 99%

Differential Evolution Using Enhanced Mutation Strategy Based on Random Neighbor Selection

Baig,

Abbas,

Ahmad

et al. 2023

Symmetry

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show abstract

“…EAs: differential evolutionary (DE) [21], evolutionary programming (EP) [22], neighborhood-based differential evolution algorithm with direction induced strategy (NDIDE) [23], genetic algorithm (GA) [24], real-coded genetic algorithm with random walk-based mutation (RCGA-CRWM) [25], crisscross optimization algorithm (COA) [26], and stochastic fractal search (SFS) algorithm [27] • Swarm intelligence-based algorithms: grasshopper optimization algorithm (GOA) [6], bee colony optimization (BCO) algorithm [28], adaptive cuckoo search with differential evolution mutation (ACS-DEM) [29], wall optimization algorithm (WOA) [30], cuckoo search algorithm (CSA) [31], group search optimization (GSO) [32], wild goats algorithm (WGA) [33], particle swarm optimization (PSO) [34], firefly algorithm (FA) [35], invasive weed optimization (IWO) algorithm [36], marine predators algorithm (MPOA) [37], and artificial bee colony (ABC) [38] •…”

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confidence: 99%

Solving the Combined Heat and Power Economic Dispatch Problem in Different Scale Systems Using the Imperialist Competitive Harris Hawks Optimization Algorithm

Nazari,

Abdi

2023

Biomimetics

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The aim of electrical load dispatch (ELD) is to achieve the optimal planning of different power plants to supply the required power at the minimum operation cost. Using the combined heat and power (CHP) units in modern power systems, increases energy efficiency and, produce less environmental pollution than conventional units, by producing electricity and heat, simultaneously. Consequently, the ELD problem in the presence of CHP units becomes a very non-linear and non-convex complex problem called the combined heat and power economic dispatch (CHPED), which supplies both electric and thermal loads at the minimum operational cost. In this work, at first, a brief review of optimization algorithms, in different categories of classical, or conventional, stochastic search-based, and hybrid optimization techniques for solving the CHPED problem is presented. Then the CHPED problem in large-scale power systems is investigated by applying the imperialist competitive Harris hawks optimization (ICHHO), as the combination of imperialist competitive algorithm (ICA), and Harris hawks optimizer (HHO), for the first time, to overcome the shortcomings of using the ICA and HHO in the exploitation, and exploration phases, respectively, to solve this complex optimization problem. The effectiveness of the combined algorithm on four standard case studies, including 24 units as a medium-scale, 48, 84, units as the large-scale, and 96-unit as a very large-scale heat and power system, is detailed. The obtained results are compared to those of different algorithms to demonstrate the performance of the ICHHO algorithm in terms of better solution quality and lower fuel cost. The simulation studies verify that the proposed algorithm decreases the minimum operation costs by at least 0.1870%, 0.342%, 0.05224%, and 0.07875% compared to the best results in the literature.

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Optimization of economic dispatch using updated differential evolution algorithm

Tiwari,

Mishra,

Parouha

2024

Int. j. inf. tecnol.

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Neighborhood-based differential evolution algorithm with direction induced strategy for the large-scale combined heat and power economic dispatch problem

Cited by 16 publications

References 41 publications

Differential Evolution Using Enhanced Mutation Strategy Based on Random Neighbor Selection

Differential Evolution Using Enhanced Mutation Strategy Based on Random Neighbor Selection

Solving the Combined Heat and Power Economic Dispatch Problem in Different Scale Systems Using the Imperialist Competitive Harris Hawks Optimization Algorithm

Optimization of economic dispatch using updated differential evolution algorithm

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