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Self-adaptive differential evolution with Gaussian–Cauchy mutation for large-scale CHP economic dispatch problem

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Abstract

With the widespread application of co-generation units, the combined heat and power economic dispatch (CHPED) has become an important issue in the power system operation. Existing research work mostly focuses on small- or medium-scale CHPED problem, and there is very little research work on large-scale CHPED problems. Considering the characteristics of high-dimensional variables and huge search space in large-scale CHPED problem, it brings great challenge to the existing algorithms. In this paper, an improved differential evolution algorithm, called self-adaptive differential evolution with Gaussian–Cauchy mutation (SDEGCM), is proposed to solve the large-scale CHPED problem. In SDEGCM, in order to improve the performance, two strategies namely Gaussian–Cauchy mutation and parameter self-adaptation are introduced. Moreover, a constraint repair technique is used in SDEGCM to deal with complex operating constraints. The SDEGCM is applied to solve three large-scale CHPED problems with 48, 84 and 96 units, and compared with three well-established differential evolution and other methods in the literature. It is found that the proposed SDEGCM has advantages in terms of solution accuracy and stability for the large-scale CHPED problem.

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Abbreviations

ABC:

Artificial bee colony

BA:

Bat algorithm

BLPSO:

Biogeography-based learning particle swarm optimization

CHP:

Combined heat and power

CHPED:

Combined heat and power economic dispatch

COA:

Cuckoo optimization algorithm

CSA:

Cuckoo search algorithm

CSA-BA-ABC:

Hybridizing bat algorithm and artificial bee colony with chaotic based self-adaptive search

CSO:

Crisscross optimization algorithm

DE:

Differential evolution

DEGM:

Differential evolution with Gaussian mutation

ECSA:

Effective cuckoo search algorithm

FA:

Firefly algorithm

FSRPSO:

Hybrid firefly and self-regulating particle swarm optimization

GA:

Genetic algorithm

GCM:

Gaussian–Cauchy mutation strategy

GSA:

Gravitational search algorithm

GSO:

Group search optimization

HS:

Harmony search

HTS:

Heat transfer search

IABC:

Improved artificial bee colony

IGA-NCM:

Improved genetic algorithm using novel crossover and mutation

JADE:

Adaptive differential evolution with optional external archive

KKO:

Kho-kho optimization

LHS:

Latin hypercube sampling

MADS:

Mesh adaptive direct search algorithm

MPHS:

Multi-player harmony search method

PSA:

Parameter self-adaptive strategy

PSO:

Particle swarm optimization

RCGA-CRWM:

Real-coded genetic algorithm with random walk-based mutation

RCGA-IMM:

Real-coded genetic algorithm with improved Muhlenbein mutation

SABBO:

Biogeography-based optimization with simulated annealing

SaDE:

Self-adaptive differential evolution

SDEGCM:

Self-adaptive differential evolution with Gauss and Cauchy mutation

SFO:

Sailfish optimization

SFS:

Stochastic fractal search

SRPSO:

Self-regulating particle swarm optimization

TCSO:

Social cognitive optimization with tent map

TVAC-GSA-PSO:

Hybrid gravitational search algorithm-particle swarm optimization with time varying acceleration coefficients

TVAC-PSO:

Particle swarm optimization with time varying acceleration coefficients

WOA:

Whale optimization algorithm

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Author information

Authors and Affiliations

  1. School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, 212013, Jiangsu, China

    Xu Chen & Anning Shen

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  1. Xu Chen

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  2. Anning Shen

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Correspondence toXu Chen.

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Chen, X., Shen, A. Self-adaptive differential evolution with Gaussian–Cauchy mutation for large-scale CHP economic dispatch problem.Neural Comput & Applic34, 11769–11787 (2022). https://doi.org/10.1007/s00521-022-07068-w

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