## 1 Introduction  
The real-life optimization problems are often unpredictable and dynamic, which means that the optimization problems are facing many time-varying and unimaginable constraints, such as routing problems, scheduling problems, prediction and more variants of broad application problems. The primary purpose of the optimization problems is to find the most optimum minimum or maximum value in the search space where often the problems have more than one maximum or minimum point. This type of problem is considered an NP-hard (nondeterministic polynomial time) problem, requiring high computational processing to solve it. Currently, evolutionary computation (EC) is widely used in numerous studies to solve this type of problem. The EC technique has proven to provide an effective search in a complex search to achieve optimal or near-optimal solutions and has been proven to have strong global search ability [1]. As a classic heuristic method, Particle Swarm Optimization (PSO) is one of the most used and reliable swarm intelligence techniques [2]. It has been successfully adopted into many practical applications due to its efficiency, fast optimization speed and simplicity of implementation. In the traditional PSO, each particle of the population is learning from its nearest neighbors, and this may cause the particle swarm to stagnate in the local region due to rapid convergence [1]. Unlike the traditional PSO, one of its variants, the Fully Informed Particle Swarm Optimization, FIPS, introduced by [PERSON], [PERSON] and [PERSON] in 2004, has the weight contributions of all particles to have the same value. The particles are influenced by their whole neighborhood in a specific way according to different neighborhood topologies. This technique enables each particle to access the most successful solutions from the whole swarm, not necessarily the best from its nearest neighbor. Accordingly, the performance of FIPS algorithm types is also generally more dependent on the neighborhood topology [3]. Unlike PSO and its variants, Differential Evolution (DE) is also a widely used algorithm with a remarkable ability to find the optimal solution. DE relies upon its strength in handling starting initial points where multiple starting points are randomly chosen during sampling of potential solutions [4; 5]. Several versions of the proposed adaptive DE focus on manipulating the parameter of DE with the introduction of a new way of controlling the value of existing parameters. The adaptive differential evolution (jDE) [6] implemented several DE strategies to control the diversity of the population. Additionally, it can self-adapt the scaling factor \(F\) and the crossover rate \(C_{r}\). The parameter adaptive differential evolution (JADE) [7] relies on greedy mutation strategy (DE/current-to-_p_best) with optimal external achieve and utilizing the previously explored inferior solutions. In SaDE [8], suitable learning strategies and parameter settings are gradually self-adapted according to the previous learning experiences. Even more, the choice of learning strategy and the two essential control parameters of DE, \(F\) and \(C_{r}\), are not required to be specified before the evolution phase. In the past few years, the development of adaptive mechanisms has emerged to be one of the critical issues in the branch of the EC algorithm. Adaptive mechanism refers to the ability of the algorithm to change its behavior according to information available during its running phase. The number of works in which adaptive mechanisms are being successfully used has increased enormously over the past few decades. The applications include a wide variety of areas, such as routing problems, signal processing, optimization problem and medical fields. Furthermore, the efficiency of the adaptive mechanism mainly depends on the algorithm design and the algorithm used for adaptation. One of the common drawbacks EC algorithms face in solving optimization problems is the lack of diversity in solutions causing a suboptimal solution.