International Journal of Leading Research Publication

E-ISSN: 2582-8010   •   Impact Factor: 9.56

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 6 Issue 4 April 2025 Submit your research before last 3 days of to publish your research paper in the issue of April.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 6 Issue 4
April 2025

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

Performance Analysis of Bio-Inspired Algorithms for Energy Resource Allocation

Author(s) Jaymin Pareshkumar Shah
Country India
Abstract In response to the rising need to enhance energy resource allocation in multiple areas, from smart grids to data centers, researchers have been working towards finding innovative approaches that can boost resource management. Bio-inspired methods that imitate natural processes and their behaviors have been presented as valuable techniques for tackling challenging optimal problems. This paper carries out a detailed performance evaluation of several bio-inspired algorithms, including Genetic Algorithms (GAs), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO) in asset-based energy resource scheduling. This program seeks to find the most effective and efficient algorithms in different handlesby assessing their performance in various scenarios.
Comparative analysis implies thorough testing using the benchmark data, imitating the realistic energy allocation tasks. The key performance metrics – such as convergence speed, the quality of the solution, and the computational efficiency – are evaluated to get an overall view of the capacity of every algorithm. The outcomes show that while all bio-inspired heuristics have special virtues, the performance of each heavily counts within the distinct nature of the challenge posed. For example, in very complex environments, GAs bring robust performance, and in scenarios with low convergence restrictions, PSO presents better performance. At the same time, ACO likes options in unstable climates where capability is the upper hand.
This research provides significant suggestions on the practicability of bio-inspired algorithms for energy resource scheduling. By revealing the pros and cons of every algorithm, this paper acts as a resource for practitioners and researchers who desire to put in place the best energy management strategies. Moreover, the findings emphasize the necessity to correctly choose the evolutionary algorithm based on the particular characteristics of a considered problem, thus increasing the efficiency of energy resource-allocating systems. This study contributes to the growth of the knowledge on bio-inspired optimization methods and opens the possibility for further research studies of the efficient use of renewable energies.
Field Engineering
Published In Volume 4, Issue 8, August 2023
Published On 2023-08-02
Cite This Performance Analysis of Bio-Inspired Algorithms for Energy Resource Allocation - Jaymin Pareshkumar Shah - IJLRP Volume 4, Issue 8, August 2023. DOI 10.70528/IJLRP.v4.i8.1467
DOI https://doi.org/10.70528/IJLRP.v4.i8.1467
Short DOI https://doi.org/g898vt
View / Download PDF File

Share this



CrossRef

CrossRef DOI is assigned to each research paper published in our journal.

IJLRP DOI prefix is
10.70528/IJLRP

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 5 Isu 12 Cover Page Vol 5 Isu 11 Cover Page Vol 5 Isu 10

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJLRP
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us +91-9687-828-838 editor@ijlrp.com