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|Title:||Society and civilization: An optimization algorithm based on the simulation of social behavior||Authors:||Ray, T.
|Issue Date:||Aug-2003||Citation:||Ray, T., Liew, K.M. (2003-08). Society and civilization: An optimization algorithm based on the simulation of social behavior. IEEE Transactions on Evolutionary Computation 7 (4) : 386-396. ScholarBank@NUS Repository. https://doi.org/10.1109/TEVC.2003.814902||Abstract:||The ability of an individual to mutually interact is a fundamental social behavior that is prevalent in all human and insect societies. Social interactions enable individuals to adapt and improve faster than biological evolution based on genetic inheritance alone. This is the driving concept behind the optimization algorithm introduced in this paper that makes use of the intra and intersociety interactions within a formal society and the civilization model to solve single objective constrained optimization problems. A society corresponds to a cluster of points in the parametric space while a civilization is a set of all such societies at any given point of time. Every society has its set of better performing individuals (henceforth, referred as leaders) that help others in the society to improve through an intrasociety information exchange. The intrasociety information exchange results in the migration of a point toward a better performing point in the cluster that is analogous to an intensified local search around a better performing point. Leaders of a society on the other hand improve only through an intersociety information exchange that results in the migration of a leader from a society to another that is headed by better performing leaders. This process of leader migration helps the better performing societies to expand and flourish that correspond to a search around globally promising regions in the parametric space. In order to study the performance of the proposed algorithm, four well-studied, single objective constrained engineering design optimization problems have been solved. The results indicate that the algorithm is capable of arriving at comparable solutions using significantly fewer function evaluations and stands out as a promising alternative to existing optimization methods for engineering design. Futhermore, the algorithm employs a novel nondominance scheme to handle constraints that eliminates the problem of scaling and aggregation that is common among penalty-function-based methods.||Source Title:||IEEE Transactions on Evolutionary Computation||URI:||http://scholarbank.nus.edu.sg/handle/10635/111482||ISSN:||1089778X||DOI:||10.1109/TEVC.2003.814902|
|Appears in Collections:||Staff Publications|
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