2024

Gajić, Milena 
Arsić, Sanela 
Radosavljević, Jordan
Jevtić, Miroljub
Perović, Bojan
Klimenta, Dardan
Milovanović, Miloš

Taylor & Francis Group

Applied Artificial Intelligence

38

1

2322335

English

M22 - Paper in a prominent international journal

This paper proposes a new metahaeuristic algorithm named particle swarm optimization and chaotic gravitational search algorithm (PSO-CGSA) for solving the combined economic and emission dispatch (CEED) problem. First, we determine the efficiency and effectiveness measures of the algorithm and compare it with other well-known algorithms. Then, we analyze the obtained solutions using the statistical procedure proposed in the paper. The proposed procedure contains the following: (i) the behavior analysis of the algorithms when solving the CEED problem, using non-parametric tests, and (ii) the ranking of the algorithms using the PROMETHEE/GAIA multi-criteria decisionmaking method. The behavior analysis is performed for two cases: (i) when solving individual variants of the CEED problem (single-problem analysis) and (ii) when solving a set of CEED variants (multiple-problem analysis). The results of the applied procedure for the test system with six generators show that PSO-CGSA has (i) the best solution for each tested variant of the CEED problem; (ii) the best standard deviation, mean value, error rate, and behavior for the CEED variant with a bi-objective function that simultaneously minimizes fuel cost and emission, taking into account the valve point effect; and (iii) the best rank when solving a set of CEED variants.

combined economic emission dispatch (CEED), metaheuristics, multi-criteria decision making (MCDM), non-parametric tests, particle swarm optimization

DOI

10.1080/08839514.2024.2322335

ISSN

1087-6545
0883-9514

URI

https://repozitorijum.tfbor.bg.ac.rs/handle/...