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It is well recognized that human societies and their problem-solving capabilities have evolved much faster than biological evolution. The inspiration from human behaviors, knowledge exchange, and transformation has given rise to a new evolutionary computation paradigm. Multiple research endeavors have been reported in the literature inspired by the diverse aspects of human societies with corresponding terminologies to describe the algorithm. These endeavors have resulted in piles of algorithms, worded differently but with more or less similar underlying mechanisms causing immense confusion for a new reader. This paper presents a generalized framework for these socio-inspired evolutionary algorithms (SIEAs) or socio-inspired meta-heuristic algorithms. A survey of various SIEAs is provided to highlight the working of these algorithms on a common framework, their variations and improved versions proposed in the literature, and their applications in various fields of search and optimization. The algorithmic description and the comparison of each SIEA with the general framework enable a clearer understanding of the similarities and differences between these methodologies. Efforts have been made to provide an extensive list of references with due context. This paper could become an excellent reference as a starting point for anyone interested in this fascinating field of research. [ABSTRACT FROM AUTHOR]

Real-parameter single objective optimization has been studied for decades. In recent, a new setting is applied in this field based on the consideration that solving difficulty scales exponentially with the increase in dimensionality. Under the new setting, differential evolution (DE) still outstands in performance as before. Meanwhile, a new type of population-based metaheuristic—gaining–sharing knowledge-based algorithm, becomes a dark horse. Furthermore, ensemble of the above two types of algorithm is proposed in the literature. Although such ensemble shows good performance, provided that a more reasonable scheme is used for the communication between the constituent algorithms, better ensemble can be obtained. We believe that the new scheme should be with adaptiveness. In this paper, we propose an adaptive scheme for the communication. According to the scheme, individuals chosen based on fitness and lifetime are exchanged. In fact, in the field of DE, it is rare to consider lifetime of individual. However, lifetime is no less important than fitness in our scheme. In our experiment, our ensemble is compared with seven state-of-the-art algorithms. According to experimental results, our ensemble is comparable to one of the peers and better than the other ones. [ABSTRACT FROM AUTHOR]

Research that focuses on the performance improvement of metaheuristics is important to gain understanding on the characteristic of specific algorithm. Better understanding of metaheuristics method will bring benefit when it comes to implement the methods to solve real problems. This paper studies the effect of local information sharing on the performance of soccer game optimization with suitable control parameter settings. A novel method, called soccer game optimization with local information sharing (SGOLS), has been proposed in this paper. The method implements local information derived from several nearby players to conduct move forward. Therefore, the move forward will consider the position of the ball dribbler, the previous best player position as well as the position of players nearby. The proposed method is evaluated based on 20 unconstraint continuous problems, consisting of unimodal function and multimodal functions, and compared to SGO, PSO and DE. The experiment result reveals that the local information sharing could enhance intensification search in SGOLS. The proposed methods perform better in high-dimensionality problems than SGO, PSO and DE. However, the use of local information consumes more computational times than the SGO and PSO, but it is still faster than DE. [ABSTRACT FROM AUTHOR]

The gaining sharing knowledge based optimization algorithm (GSK) is recently developed metaheuristic algorithm, which is based on how humans acquire and share knowledge during their life-time. This paper investigates a modified version of the GSK algorithm to find the best feature subsets. Firstly, it represents a binary variant of GSK algorithm by employing a probability estimation operator (Bi-GSK) on the two main pillars of GSK algorithm. And then, the chaotic maps are used to enhance the performance of the proposed algorithm. Ten different types of chaotic maps are considered to adapt the parameters of the GSK algorithm that make a proper balance between exploration and exploitation and save the algorithm from premature convergence. To check the performance of proposed approaches of GSK algorithm, twenty-one benchmark datasets are taken from the UCI repository for feature selection. The performance is measured by calculating different type of measures, and several metaheuristic algorithms are adopted to compare the obtained results. The results indicate that Chebyshev chaotic map shows the best result among all chaotic maps which improve the performance accuracy and convergence rate of the original algorithm. Moreover, it outperforms the other metaheuristic algorithms in terms of efficiency, fitness value and the minimum number of selected features. [ABSTRACT FROM AUTHOR]