A ranking-based fuzzy adaptive hybrid crow search algorithm for combined heat and power economic dispatch

Ramachandran, Murugan; Mirjalili, Seyedali; Malli Ramalingam, Mohan; Asir Rajan Charles Gnanakkan, Christober; Sundari Parvathysankar, Deiva; Sundaram, Arunachalam

Abstract

This paper attempts to obtain optimal generation scheduling for Combined Heat and Power Economic Dispatch (CHPED) problems and seeking a possible solution for the global optimization of test systems. As such, a Fuzzy adaptive Ranking- based Crow Search Algorithm (FRCSA) is amalgamated with modified Artificial Bee Colony (ABC). The proposed algorithm (FRCSA-ABC) has been integrated with three mechanisms in order to achieve competitive results effectively. The first mechanism uses the Fuzzy logic Inference System (FIS) to tune dynamically the parameters of flight length (fl) and awareness probability (CAP) of each crow. The second mechanism emerges from a natural phenomenon known as proximate optimality principle (POP) which reveals that better individuals will often have beneficial information and more probabilities to select and guide other individuals. This observation leads to the feasibility-based Ranking Crow Search Algorithm (RCSA) which generates a new food source from the chosen higher rankings of parent food sources. The third mechanism integrates the two modified global search phases of ABC with a local search phase of FRCSA to ensure promising performance. During the evaluation process, this mechanism investigates the aging level of the individual’s best solution (pbest) in order to choose an appropriate search phase in FRCSA and two modified phases of ABC. The performance of the algorithm is tested on four CHPED test systems, 23 well-known benchmark functions, and test suit of CEC2017. The results obtained are compared with its variants as well as with existing different approaches to validate its optimal search efficiency. Further, non-parametric statistical tests such as pair-wise and multiple comparisons tests are adapted to establish the supremacy of the FRCSA-ABC. The seminal aspect of this intended algorithm is that it can achieve cost-effectiveness conforming to meticulous global convergence.

Citation

Ramachandran, M., Mirjalili, S., Malli Ramalingam, M., Asir Rajan Charles Gnanakkan, C., Sundari Parvathysankar, D., & Sundaram, A. (2022). A ranking-based fuzzy adaptive hybrid crow search algorithm for combined heat and power economic dispatch. Expert systems with applications, 197, Article 116625. https://doi.org/10.1016/j.eswa.2022.116625

• Journal Article Type: Article
• Acceptance Date: Jan 28, 2022
• Online Publication Date: Feb 22, 2022
• Publication Date: 2022-07
• Deposit Date: Jul 17, 2024
• Journal: Expert Systems with Applications
• Print ISSN: 0957-4174
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 197
• Article Number: 116625
• DOI: https://doi.org/10.1016/j.eswa.2022.116625
• Keywords: Combined heat and power economic dispatch,Crow search algorithm,Artificial bee colony algorithm,Adaptive ranking selection,Fuzzy logic,Sine-Cosine