Socio evolution & learning optimization algorithm: A socio-inspired optimization methodology

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Abstract

The paper proposes a novel metaheuristic Socio Evolution & Learning Optimization Algorithm (SELO) inspired by the social learning behaviour of humans organized as families in a societal setup. This population based stochastic methodology can be categorized under the very recent and upcoming class of optimization algorithms—the socio-inspired algorithms. It is the social tendency of humans to adapt to mannerisms and behaviours of other individuals through observation. SELO mimics the socio-evolution and learning of parents and children constituting a family. Individuals organized as family groups (parents and children) interact with one another and other distinct families to attain some individual goals. In the process, these family individuals learn from one another as well as from individuals from other families in the society. This helps them to evolve, improve their intelligence and collectively achieve shared goals. The proposed optimization algorithm models this de-centralized learning which may result in the overall improvement of each individual’s behaviour and associated goals and ultimately the entire societal system. SELO shows good performance on finding the global optimum solution for the unconstrained optimization problems. The problem solving success of SELO is evaluated using 50 well-known boundary-constrained benchmark test problems. The paper compares the results of SELO with few other population based evolutionary algorithms which are popular across scientific and real-world applications. SELO’s performance is also compared to another very recent socio-inspired methodology—the Ideology algorithm. Results indicate that SELO demonstrates comparable performance to other comparison algorithms. This gives ground to the authors to further establish the effectiveness of this metaheuristic by solving purposeful and real world problems.

Section snippets

Introduction and motivation

Optimization methods play a vital role in solving engineering problems. The exact optimization methods or deterministic methods may not be computationally efficient in solving complex nonlinear and multimodal problems that exist in most real-world applications [[1], [2]]. In the past few decades researchers have resorted to a number of methodologies inspired from biological and natural systems have been proposed for solving complex optimization problems. By far the majority of nature-inspired

Methodology : Socio evolution & learning optimization Algorithm (SELO)

The proposed iterative algorithm is population based, which initially starts its search and optimization process with a population of solutions. Akin to other population-based designs, SELO attempts to direct the population of possible solutions towards the more promising areas of the solution space in search for optimal solution. In the context of SELO, the behaviour of an individual belonging to a family represents each such solution. Each family comprises of individuals or family members,

Results and discussion

This section presents the benchmark test problems used and the results and findings in order to evaluate the performance of the proposed Social algorithm and how well it performs on finding the global optimum solution for the unconstrained problems. The section also discusses the control parameters, precision and the stopping criterion used for testing the optimization algorithm along with tabulated results. The performance of SELO is compared to other widely used population based algorithms

Conclusions and future direction

In the paper, a new socio-inspired methodology referred to as Socio Evolution and Learning Optimization (SELO) is proposed which mimics the natural social tendency of humans organized as family groups. It is motivated by the evolution of social behaviour of every individual in a family. The parents and children of a family evolve (become better) by observing and learning from one another as well as from other families. A group of families co-existing together may be called as a society. In this

Anand J. Kulkarni holds a Ph.D. in Distributed Optimization from Nanyang Technological University, Singapore, M.S. in Artificial Intelligence from University of Regina, Canada, Bachelor of Engineering from Shivaji University, India and Diploma from the Board of Technical Education, Mumbai. He worked as a research fellow on a cross-border supply-chain disruption project at Odette School of Business, University of Windsor, Canada. Currently, he is working as Head and Associate Professor at the

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    Anand J. Kulkarni holds a Ph.D. in Distributed Optimization from Nanyang Technological University, Singapore, M.S. in Artificial Intelligence from University of Regina, Canada, Bachelor of Engineering from Shivaji University, India and Diploma from the Board of Technical Education, Mumbai. He worked as a research fellow on a cross-border supply-chain disruption project at Odette School of Business, University of Windsor, Canada. Currently, he is working as Head and Associate Professor at the Symbiosis Institute of Technology, Symbiosis International University, Pune, India. His research interests include optimization algorithms, multi-objective optimization, continuous, discrete and combinatorial optimization, multiagent systems, complex systems, cohort intelligence, probability collectives, swarm optimization, game theory, self-organizing systems and fault-tolerant systems. He is the founder and chairman of the Optimization and Agent Technology (OAT) Research Lab. Anand has published over 30 research papers in peer-reviewed journals and conferences. He also published two research books.

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