Hybrid multi‐objective optimization algorithm using Taylor series model and Spider Monkey Optimization

• Published in: International journal for numerical methods in engineering 2021-05, Vol.122 (10), p.2478-2497
• Main Authors: Menon, Radhika, Kulkarni, Anju, Singh, Deepak, Venkatesan, Mithra
• Format: Article
• Language: English
• Subjects: Algorithms; Iterative methods; Neural networks; Optimization; predictive theory; Resource allocation; Spider Monkey Optimization; Taylor series
• ISSN: 0029-5981; 1097-0207
• DOI: 10.1002/nme.6628

Multi‐objective optimization is used for optimizing a number of objectives simultaneously. Mostly, the optimization algorithms considered the previous iterative position to find the next position updates. The main intention of this research is to design and develop a new model to solve the computational complexity, and the resource allocation problem. Based on this perspective, the Taylor series model and its predictive theory are applied to Spider Monkey Optimization (SMO), and a new optimization, named Taylor‐Spider Monkey Optimization (TaySMO) is developed. The proposed TaySMO computes the updated position of the swarm using the local leader phase and the global leader phase. However, a new position update equation is derived to enhance the searching process of the SMO. Here, multiple objectives such as, throughput, power, and fairness index are considered to solve the resource allocation problem. However, the performance of the proposed algorithm is evaluated using the conventional optimization function in terms of fitness function and convergence criteria as the mean square error (MSE) with the neural network learning is 0.3747, congestion rate of the resource allocation problem is 8.736E‐23, and MSE of the spectrum sensing is 8.74E‐23, respectively.