Advancements in Optimization: Critical Analysis of Evolutionary, Swarm, and Behavior-Based Algorithms

The research work on optimization has witnessed significant growth in the past few years, particularly within multi- and single-objective optimization algorithm areas. This study provides a comprehensive overview and critical evaluation of a wide range of optimization algorithms from conventional me...

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Vydáno v:Algorithms Ročník 17; číslo 9; s. 416
Hlavní autoři: Rashed, Noor A., Ali, Yossra H., Rashid, Tarik A.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.09.2024
Témata:
Algorithms
Behavior
Bibliometrics
Content analysis
Evolutionary algorithms
Genetic algorithms
Heuristic methods
Literature reviews
Mathematical optimization
metaheuristic algorithms
multi-objective optimization
Multiple objective analysis
Objectives
Optimization algorithms
Optimization techniques
Particle swarm optimization
Performance evaluation
R&D
Research & development
scheduling
Search strategies
single-objective optimization
Students
Trends
ISSN:1999-4893, 1999-4893
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Shrnutí:The research work on optimization has witnessed significant growth in the past few years, particularly within multi- and single-objective optimization algorithm areas. This study provides a comprehensive overview and critical evaluation of a wide range of optimization algorithms from conventional methods to innovative metaheuristic techniques. The methods used for analysis include bibliometric analysis, keyword analysis, and content analysis, focusing on studies from the period 2000–2023. Databases such as IEEE Xplore, SpringerLink, and ScienceDirect were extensively utilized. Our analysis reveals that while traditional algorithms like evolutionary optimization (EO) and particle swarm optimization (PSO) remain popular, newer methods like the fitness-dependent optimizer (FDO) and learner performance-based behavior (LPBB) are gaining attraction due to their adaptability and efficiency. The main conclusion emphasizes the importance of algorithmic diversity, benchmarking standards, and performance evaluation metrics, highlighting future research paths including the exploration of hybrid algorithms, use of domain-specific knowledge, and addressing scalability issues in multi-objective optimization.
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ISSN:1999-4893
1999-4893
DOI:10.3390/a17090416