A survey on binary metaheuristic algorithms and their engineering applications.

Area of Science:

• Engineering and Computer Science
• Optimization Algorithms

Background:

• Binary metaheuristic algorithms are increasingly used in complex engineering problems.
• A comprehensive understanding of their applications and encoding methods is crucial for researchers.
• Existing literature often lacks a structured overview of these algorithms in engineering contexts.

Purpose of the Study:

• To provide a state-of-the-art investigation of engineering applications of binary metaheuristic algorithms.
• To categorize surveyed work based on application scenarios and solution encoding.
• To guide researchers in selecting appropriate metaheuristic methods for specific engineering challenges.

Main Methods:

• Systematic literature review of binary metaheuristic algorithms in engineering.
• Categorization of algorithms by application domains (e.g., feature selection, scheduling, structural optimization).
• Analysis of solution encoding techniques, with a focus on transfer functions like the Sigmoid function.

Main Results:

• Identified key engineering applications including feature selection, scheduling, layout design, and structural optimization.
• Highlighted the prevalence of Sigmoid functions as the primary transfer function for binary metaheuristic algorithms.
• Detailed various implementations and objective functions (single- and multi-objective) used in surveyed studies.

Conclusions:

• Current binary metaheuristic algorithms face challenges in complex engineering applications.
• Future research should focus on developing novel binary algorithms and transfer functions.
• Addressing time-consuming problems and improving application integration are critical for advancing the field.