A comprehensive survey of sine cosine algorithm: variants and applications

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Meta-heuristics

Background:

• The Sine Cosine Algorithm (SCA), introduced in 2016, is a meta-heuristic optimization technique.
• SCA is inspired by the mathematical properties of sine and cosine functions.
• It has gained significant research attention due to its efficiency and convergence speed.

Purpose of the Study:

• To provide a comprehensive overview of the Sine Cosine Algorithm (SCA).
• To categorize and discuss various modified, multi-objective, and hybridized versions of SCA.
• To highlight the broad applicability of SCA across different scientific and engineering domains.

Main Methods:

• Review of the foundational Sine Cosine Algorithm (SCA) principles.
• Categorization and analysis of SCA variants (modified, multi-objective, hybridized).
• Compilation of SCA applications in diverse fields.

Main Results:

• SCA demonstrates competitive performance regarding execution time, convergence rate, and efficiency.
• Various adaptations of SCA have been developed to address complex optimization challenges.
• SCA has been successfully applied to problems in classification, image processing, robotics, scheduling, and power systems.

Conclusions:

• SCA is a versatile and effective optimization tool with proven efficacy in numerous applications.
• The ongoing development of SCA variants continues to expand its problem-solving capabilities.
• Future research directions for SCA include further enhancements and novel application areas.