Evolutionary computation has gone through vast and diverse research endeavors in the past few decades. Although the initial inspiration came from Darwin’s ideas of biological evolution, the field has moved thereon to ideas from the collective intelligence of insects, birds, and fish, to name a few. A variety of algorithms have been proposed in the literature based on these ideas and have been shown to perform well in various applications. More recently, inspiration from human behavior and knowledge exchange and transformation has given rise to a new evolutionary computing paradigm. It is well recognized that human societies and problem-solving capabilities have evolved much faster than biological evolution. Many research endeavors have been reported in the literature inspired by diverse aspects of human societies with corresponding terminologies to describe the algorithm. These endeavors have resulted in a plethora of algorithms worded differently from each other, but the underlying mechanisms could be more or less similar, causing immense confusion for a new reader. This paper presents a generalized framework for these Socio-inspired Evolutionary Algorithms (SIEAs) or Socio-inspired Metaheuristic 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 of each SIEA enables 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. In that sense, this paper could become an excellent reference as a starting point for anyone interested in this fascinating field of research.

The utilization of metaheuristics for solving combinatorial optimization problems has increased considerably in the last few decades. Metaheuristics inspired by various natural phenomena have been proposed due to their optimization characteristics. Imperialist Competitive Algorithm (ICA) is one such metaheuristic inspired by the socio-political process of imperialism. ICA has become popular due to its extensive applications in various engineering domains. Originally, ICA was designed to solve continuous optimization problems. This paper presents a binary version of ICA, dubbed ICA with Binary-encoding (ICAwB), to solve selection problems. ICAwB works with binary encoding and utilizes new socio-politically inspired operators. Additional features are incorporated within ICAwB to develop an improved version of it dubbed IICAwB. ICAwB and IICAwB with other binary versions of ICA are compared. IICAwB shows much better performance than existing binary ICAs and ICAwB. The proposed IICAwB is quite generic, and its applicability to other combinatorial optimization problems can be attempted with advantage.

Evolutionary computation has gone through vast and diverse research endeavors in the past few decades. Although the initial inspiration came from Darwin's ideas of biological evolution, the field has moved thereon to ideas from the collective intelligence of insects, birds, and fish, to name a few. A variety of algorithms have been proposed in the literature based on these ideas and have been shown to perform well in various applications. More recently, inspiration from human behavior and knowledge exchange and transformation has given rise to a new evolutionary computing paradigm. It is well recognized that human societies and problem-solving capabilities have evolved much faster than biological evolution. Many research endeavors have been reported in the literature inspired by diverse aspects of human societies with corresponding terminologies to describe the algorithm. These endeavors have resulted in a plethora of algorithms worded differently from each other, but the underlying mechanisms could be more or less similar, causing immense confusion for a new reader. This paper presents a generalized framework for these Socio-inspired Evolutionary Algorithms (SIEAs) or Socio-inspired Metaheuristic 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 of each SIEA enables 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. In that sense, this paper could become an excellent reference as a starting point for anyone interested in this fascinating field of research.