Sine Cosine Algorithm for Optimization (SpringerBriefs in Applied Sciences and Technology)

This open access book serves as a compact source of information on sine cosine algorithm (SCA) and a foundation for developing and advancing SCA and its applications. SCA is an easy, user-friendly, and strong candidate in the field of metaheuristics algorithms. Despite being a relatively new metaheuristic algorithm, it has achieved widespread acceptance among researchers due to its easy implementation and robust optimization capabilities. Its effectiveness and advantages have been demonstrated in various applications ranging from machine learning, engineering design, and wireless sensor network to environmental modeling. The book provides a comprehensive account of the SCA, including details of the underlying ideas, the modified versions, various applications, and a working MATLAB code for the basic SCA. Foreword Preface Contents 1 Introduction References 2 Sine Cosine Algorithm 2.1 Description of the Sine Cosine Algorithm (SCA) 2.2 Parameters Associated with the SCA 2.3 Biases of Sine Cosine Algorithm 2.3.1 Experimental Setup 2.4 Numerical Example 2.5 Source Code Reference 3 Sine Cosine Algorithm for Multi-objective Optimization 3.1 Multi-objective Optimization Problems (MOOP) 3.2 Multi-objective Optimization Techniques (MOOT) 3.2.1 Some Concepts and Terminologies 3.2.2 Different Approaches of Solving MOOP 3.3 Multi-objective SCA 3.3.1 Aggregation-Based Multi-objective Sine Cosine Algorithm and Their Applications 3.3.2 Non-dominance Diversity-Based Multi-objective SCA and Its Applications 3.4 Conclusion References 4 Sine Cosine Algorithm for Discrete Optimization Problems 4.1 Discrete Optimization Models 4.2 Discrete Optimization Methods 4.3 Binary Versions of Sine Cosine Algorithm 4.3.1 Binary Sine Cosine Algorithm Using Round-Off Method 4.3.2 Binary Sine Cosine Algorithm Using Transfer Functions 4.3.3 Binary Sine Cosine Algorithm Using Percentile Concept 4.4 Discrete Versions of Sine Cosine Algorithm References 5 Advancements in the Sine Cosine Algorithm 5.1 Modifications in the Position Update Mechanism 5.2 Opposition-Based Learning Inspired Sine Cosine Algorithm 5.3 Quantum-Inspired Sine Cosine Algorithm 5.4 Covariance Guided Sine Cosine Algorithm 5.5 Hybridization of SCA with Other Meta-heuristics References 6 Conclusion and Further Research Directions References Index