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

Abstract

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.

Multi-Strategy Boosted Spider Monkey Optimization Algorithm for Feature Selection

“…To evaluate the performance of ISMO, the mean fitness (Mean), average classification accuracy (AC avg ), and average feature selection (FS avg ) are chosen as evaluation indicators, and the formula for each indicator is presented in Eqs. ( 20)- (22).…”

“…Kalpana et al [21] optimized control parameters in combination with exponentially weighted moving averages. Secondly, in optimizing the local and global leader position update, Menon et al [22] applied prediction theory to the local and global lead stages of population position update. Gupta et al [23] introduced a quadratic approximation operator to improve the local search ability.…”

Improved particle swarm optimizer for problems with variable evaluation time: Application to asymptotic homogenization

“…Their extensive application to engineering problems has created the need for more efficient and problem‐specific developments. In swarm intelligence (SI), an important part of metaheuristics, algorithms are based on the mimicking of natural systems and cooperative populations and showed different degrees of success in particularly challenging optimization problems 3‐7 . Particle swarm optimization (PSO), first developed by Russell C. Eberhart and J. Kennedy 8,9 is one of the most popular SI algorithms.…”