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Chaotic slime mould algorithm for economic load dispatch problems

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Abstract

The economic load dispatch (eld) problem strives to optimize the division of total power demand among the power generators under specified constraints. It is solved by scheduling the generating units of a power plant that meet the load demand with minimum generation cost while satisfying various equality and inequality constraints. Achieving global optimal points is considered difficult due to the involvement of a non-linear objective function and large search domain. The slime mould algorithm (SMA) was recently proposed to solve complex problems. Its convergence rate and capability of capturing optimal global solutions are pretty satisfactory. In this paper, a chaotic number-based slime mould algorithm (CSMA) is suggested for ELD problems the first time. Five test cases with different power demands have been considered to compare the performance of the proposed approach against SMA, salp swarm algorithm (SSA), moth flame optimizer (MFO), grey wolf optimizer (GWO), biogeography based optimizer (BBO), grasshopper optimization algorithm (GOA), multi-verse optimizer (MVO) on 6, 13, 15, 40, and 140 generators ELD problems. The experimental results show that the proposed algorithm reduces the total generation cost significantly. CSMA outperformed SMA in all test cases that justify the effectiveness of chaotic sequences used in the proposed work. Further, three statistical tests have been conducted to justify the competitiveness of the suggested approach.

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  1. Department of Computer Science and Engineering, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India

    Tribhuvan Singh

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Appendix: Abbreviations

Appendix: Abbreviations

eld :

Economic load dispatch

sma :

Slime mould algorithm

csma :

Chaotic slime mould algorithm

GWO:

Grey wolf optimizer

BBO:

Biogeography-based optimization

SSA:

Salp swarm algorithm

GOA:

Grasshopper optimization algorithm

MFO:

Moth-flame optimization

MVO:

Multi-verse optimizer

TPG:

Total power generation

P L :

Power loss

TGC:

Total generation cost

PBP:

Power balance penalty

CLP:

Capacity limits penalty

RRLP:

Ramp rate limits penalty

POZP:

Prohibited operating zones penalty

N:

Population size

D:

Number of generating units

T:

Maximum iterations

G.No.:

Generating unit number

R:

Independent runs

F t :

Total generation cost

P i :

Power generated by i th generating unit

\(P_{i}^{\min \limits }\) :

Minimum power generated by i th generating unit

\(P_{i}^{\max \limits }\) :

Maximum power generated by i th generating unit

P D :

Total power demand

Fi (Pi):

Fuel cost function of i th generator

ai, bi, ci:

Fuel cost coefficients of i th generator

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Singh, T. Chaotic slime mould algorithm for economic load dispatch problems. Appl Intell 52, 15325–15344 (2022). https://doi.org/10.1007/s10489-022-03179-y

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