Socio evolution & learning optimization algorithm: A socio-inspired optimization methodology
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
References (77)
- et al.
A survey on nature inspired metaheuristic algorithms for partitional clustering
Swarm Evol. Comput.
(2014) - et al.
A review of nature-inspired algorithms
J. Bionic Eng.
(2010) - et al.
Global optimization of statistical functions with simulated annealing
J. Econometrics
(1994) - et al.
Soccer league competition algorithm: A novel meta-heuristic algorithm for optimal design of water distribution networks
Swarm Evol. Comput.
(2014) - et al.
Particle swarm optimization (PSO)
- et al.
On the performance of artificial bee colony (ABC) algorithm
Appl. Soft Comput.
(2008) Backtracking search optimization algorithm for numerical optimization problems
Appl. Math. Comput.
(2013)- et al.
A survey on the imperialist competitive algorithm metaheuristic: Implementation in engineering domain and directions for future research
Appl. Soft Comput.
(2014) - et al.
Election campaign optimization algorithm
Procedia Comput. Sci.
(2010) - et al.
Teaching–learning-based optimization: an optimization method for continuous non-linear large scale problems
Inform. Sci.
(2012)
Soccer league competition algorithm for solving knapsack problems
Swarm Evol. Comput.
Social learning optimization (SLO) algorithm paradigm and its application in QoS-aware cloud service composition
Inform. Sci.
Probability collectives: a multi-agent approach for solving combinatorial optimization problems
Appl. Soft Comput.
A comparative analysis of selection schemes used in genetic algorithms
Cultural evolution algorithm for global optimizations and its applications
J. Appl. Res. Technol.
Differential evolution with dynamic stochastic selection for constrained optimization
Inform. Sci.
Essentials of Metaheuristics, Lulu
Metaheuristics: From Design to Implementation, Vol. 74
Nature-Inspired Metaheuristic Algorithms
Clever Algorithms: Nature-Inspired Programming Recipes
A Brief Review of Nature-Inspired Algorithms for Optimization
Swarm Intelligence: From Natural to Artificial Systems, No. 1
Swarm Intelligence: Principles, Advances, and Applications
Ant colony optimization
IEEE Comput. Intell. Mag.
Fish school search
Physics-inspired optimization algorithms: a survey
J. Optim.
Optimization using simulated annealing
Statistician
Optimization by simulated annealing
Science
State-of-the-art in the structure of harmony search algorithm
Harmony search as a metaheuristic algorithm
Chemical-reaction-inspired metaheuristic for optimization
IEEE Trans. Evol. Comput.
HandBook of Metaheuristics, Vol. 2
Particle swarm optimization
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
IEEE Trans. Evol. Comput.
The CMA evolution strategy: a comparing review
Covariance matrix adaptation for multi-objective optimization
Evol. Comput.
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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.