## 1 Introduction  
The term 'optimization' refers to the best solution for a problem with minimum cost in aspect of memory, time, and resources. Sometimes processing time is fast but it may be using a lot of memory while sometimes the processing speed and memory both work fine but the accuracy may get affected. Optimization targets the best solution of any problem [1]. The solution is considered to be the best solution if it is satisfactory in terms of processing speed, resource utilization, and accuracy of the result [2]. Optimization algorithms are utilized to determine the problems of local and global search. A typical target behind the utilization of these optimization algorithms is to discover the optima for contribution as indicated by known inputs model that describes the problem which is to be solved [3]. Optimization algorithms have turned out to be the most generally adopted algorithms that are operational in all application areas, like, enterprises, sports, medical, agriculture, and finance [4]. Evolutionary algorithms (EAs) have been introduced and strongly employed in the different field of science and engineering tracks [5]. EAs have been broadly utilized to determine optimization problems of maximization and minimization to find best optimal value. Rather than ordinary strategies dependent on mathematical programming or formal rationales, EAs are observed to be all the more dominant and adaptable [6]. Despite this, in solving the complex optimization problems, EAs faces the problem of local optima in which the computation to be caught in nearby local optima and a resist the convergence speed, for example, complex nonlinear problems [7]. To enhance the performance of EAs and to avoid premature convergence, there is a need to develop new variants of evolutionary algorithms. Furthermore, dependent on genetic evolution procedures, several researchers have been given the task to improve existing EAs or developing new EAs. The most generally used EAs algorithms involve the genetic algorithm (GA) [8] and differential evolution [9]. DE is recognized as a simplistic yet strong evolutionary algorithm that has been utilized to tackle different hard optimization problems in various science and engineering disciplines [10]. As another component of an EA, the DE algorithm yields a comparative structure [11] by EA, which incorporates three essential basic genetic operators, i.e., mutation, crossover, and selection. These genetic operators contribute major roles to the performance of the DE [12]. The intelligent attitude of non-intelligent species like ants (going for searching food) or birds (during flying in flocks) or fish in school is termed as swarm intelligence (SI). SI inspired by the experience of ants, bees, birds, and fishes to fulfill their goals as a swarm [13]. If every member of a swarm in SI works individually without social interaction, it will become complicated to achieve their goals due to their individuality which results in lack of intelligence. However, when they cooperate with each other, their social interaction is improved, and they also interact with the environment which makes it easier for them to accomplish difficult tasks [14]. SI-based algorithms are ant colony optimization (ACO), bat algorithm (BA) [15; 16; 17; 18], and particle swarm optimization (PSO) [19]. PSO [20] has pulled in much consideration because of its simplicity of execution and strong search abilities. It is impelled by the social foraging fashions of fish and birds that seek for food in the form of groups. The major issue with these meta-heuristic algorithms while applying those complex numerical optimization problems is premature convergence [21]. Regardless of the nature of the non-linear problem, this issue is confronted while running a heuristic algorithm, for example, meta-heuristic algorithms like PSO [22] and DE get stuck in the local optima after little number of epochs.