An Improved Sine Cosine Algorithm for Solving Optimization Problems

Due to its simplicity and less tedious parameter tuning over other multi-agent-based optimization algorithms, Sine Cosine Algorithm (SCA) has gotten lots of attention from numerous researchers for resolving optimization problem. However, the existing SCA tends to have low optimization precision and local minima trapping effect due to the constraint in its exploration and exploitation mechanism. To overcome this drawback, an extensive version of SCA named Improved Sine Cosine Algorithm (iSCA) has been proposed in this work. The main concept is to introduce a nonlinear control strategy to the existing SCA’s exploration and exploitation process in order to synthesize the algorithm’s strength. The efficiency of this suggested algorithm is assessed using 23 classical well-known benchmark functions and the results are then verified by a comparative study with several other algorithms namely Ant Lion Optimizer (ALO), Multi-verse Optimization (MVO), Spiral Dynamic Optimization Algorithm (SDA) and Sine Cosine Algorithm (SCA). Experimental results show that the iSCA is very competitive compared to the state-of-the-art meta-heuristic algorithms.

[1]  Hossein Nezamabadi-pour,et al.  GSA: A Gravitational Search Algorithm , 2009, Inf. Sci..

[2]  Shailesh Tiwari,et al.  Physics-Inspired Optimization Algorithms: A Survey , 2013 .

[3]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[4]  Abdolreza Hatamlou,et al.  Black hole: A new heuristic optimization approach for data clustering , 2013, Inf. Sci..

[5]  Mostafa Meshkat,et al.  A novel sine and cosine algorithm for global optimization , 2017, 2017 7th International Conference on Computer and Knowledge Engineering (ICCKE).

[6]  Seyedali Mirjalili,et al.  SCA: A Sine Cosine Algorithm for solving optimization problems , 2016, Knowl. Based Syst..

[7]  A. N. K. Nasir,et al.  Novel adaptive spiral dynamics algorithms for global optimization , 2013, 2012 IEEE 11th International Conference on Cybernetic Intelligent Systems (CIS).

[8]  Mohamed Cheriet,et al.  Curved Space Optimization: A Random Search based on General Relativity Theory , 2012, ArXiv.

[9]  A. Kaveh,et al.  A new meta-heuristic method: Ray Optimization , 2012 .

[10]  Marco Dorigo,et al.  Swarm intelligence: from natural to artificial systems , 1999 .

[11]  Ibrahim Eksin,et al.  A new optimization method: Big Bang-Big Crunch , 2006, Adv. Eng. Softw..

[12]  Jeng-Shyang Pan,et al.  Handwritten Arabic Manuscript Image Binarization Using Sine Cosine Optimization Algorithm , 2016, ICGEC.

[13]  Oguz Emrah Turgut,et al.  Thermal and Economical Optimization of a Shell and Tube Evaporator Using Hybrid Backtracking Search—Sine–Cosine Algorithm , 2017 .

[14]  Urvinder Singh,et al.  Modified Grey Wolf Optimizer for Global Engineering Optimization , 2016, Appl. Comput. Intell. Soft Comput..

[15]  Seyed Mohammad Mirjalili,et al.  The Ant Lion Optimizer , 2015, Adv. Eng. Softw..

[16]  Vimal J. Savsani,et al.  Multi-objective sine-cosine algorithm (MO-SCA) for multi-objective engineering design problems , 2017, Neural Computing and Applications.

[17]  Barry Webster,et al.  A Local Search Optimization Algorithm Based on Natural Principles of Gravitation , 2003, IKE.

[18]  Aboul Ella Hassanien,et al.  Sine cosine optimization algorithm for feature selection , 2016, 2016 International Symposium on INnovations in Intelligent SysTems and Applications (INISTA).