Social Cognitive Optimization (SCO) is an optimization algorithm for solving the (constrained) numerical optimization problem. SCO is a simple agentbased model based on the observational learning mechanism in human social cognition.
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Basic Description  What’s New  Problem to be solved  Setting Parameters  Output Information  References  Contact 

License information: SCO is free software; you can redistribute it and/or modify it under the terms of the Creative Commons NonCommercial License 3.0.
Problem to be solved: (constrained) numerical optimization problem (NOP), or called the nonlinear programming problem.
System Requirements: SCO is a platformindependent software developed by JAVA version 1.4 or above.
Command line (examples): $ java SCO Problem=<Problem_Name> [NAME=VALUE] …
What’s New


Version: V1.0.001 [download]:
It implements the original SCO algorithm [1] & [2].
 Setting parameters: Problem, N, T, NL.
Problem to be solved


The problem to be solved is (constrained) numerical optimization problem (NOP), or called the nonlinear programming problem.
Tips: 1) all the variable bounds must be specified, since optimal solution(s) might situate at anywhere; and 2) problem.ProblemEncoder andproblem.UnconstrainedProblemEncoder are the parental classes of all constrained (e.g.,problem.constrained.Michalewicz_G1) and unconstrained (e.g., problem.unconstrained.GoldsteinPrice) problems, respectively.
Implemented problem instances: please download from the uptodate list of source files, which will be situated in the directories: 1) problem/constrained, and 2) problem/unconstrained.
Setting parameters [NAME=VALUE]


NAME VALUE_type Range Default_Value Description Problem String * <Problem_Name> The problem to be solved //For example: problem.constrained.Michalewicz_G2 is the default value  N integer >5 70 General: The number of agents T integer >1 2000 General: The maximum learning cycles NL integer >1 3*N For the library: The number of Points //The total number of evaluation times is N*T+NL //The program outputs runtime information of the best solution every "Tout" cycles.
Output Information


//Vopt: the value of objective function; Vcon: the weighted constraint violation value (≥0): it is not outputted if Vcon≡0 since there is no violation [Summary information]: At the end, it outputs the input variables, response values, and evaluation values <Vcon, Vopt> of the best solution.
References


[1] XiaoFeng Xie, WenJun Zhang, ZhiLian Yang. Social cognitive optimization for nonlinear programming problems. International Conference on Machine Learning and Cybernetics (ICMLC). Beijing, China, 2002: 779783. [DOI]
[2] XiaoFeng Xie, WenJun Zhang. Solving engineering design problems by social cognitive optimization. Genetic and Evolutionary Computation Conference (GECCO), LNCS 3102, Seattle, WA, USA, 2004: 261262. [DOI]
[3] XiaoFeng Xie, Jiming Liu, ZunJing Wang. A cooperative group optimization system. Soft Computing, 2014, 18(3): 469495. [DOI]