Numerical optimization using organizational evolutionary algorithm
ABSTRACT In this paper, a new algorithm, organizational evolutionary algorithm (OEA), is proposed to deal with both unconstrained and constrained optimization problems. In OEA, the evolutionary operations do not act on individuals directly, but on organization. Therefore, three evolutionary operators are designed for organizations. In experiments, OEA is tested on 3 unconstrained and 6 constrained benchmark problems, and compared with three recent algorithms. The results show that OEA outperforms the three other algorithms both in the quality of solution and the computational cost.
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ABSTRACT: The original organizational evolutionary algorithm (OEA) is often trapped in local optima when optimizing multimodal functions with high dimensions. In this paper, following an analysis of the main causes of the premature convergence, it proposes a novel algorithm, called the multipoint organizational evolutionary algorithm (mOEA). To discourage the premature convergence, a crossover strategy of multiple points is designed to achieve a better diversity of leader population. Inspired by the cognition and learning physics of social swarms, an improved annexing operator enables members in an organization to either partially climb around their leader or randomly mutate within the search range. The new annexing manipulation both enhances fitness values and preserves a good diversity of member population. Experiments on six complex optimization benchmark functions with 30 or 100 dimensions and very large numbers of local minima show that, comparing with the original OEA and CLPSO, mOEA effectively converges faster, results in better optima, is more robust.
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ABSTRACT: A novel evolutionary algorithm, Organizational Evolutionary Algorithm for SATisfiability problems (OEA_SAT), is proposed in this paper. OEA_SAT first divides a SAT problem into several sub-problems, and each organization is composed of a sub-problem. Thus, three new evolutionary operators, namely the self-learning operator, the annexing operator and the splitting operator are designed with the intrinsic properties of SAT problems in mind. Furthermore, all organizations are divided into two populations according to their fitness. One is called best-population, and the other is called non-best-population. The idea behind OEA_SAT is to solve the sub-problem first, and then synthesize the solution for the original problem by adjusting the variables which have conflicts. Since the dimensions of sub-problems are smaller and the sub-ones are easy to be solved compared with the original one, the computational cost is reduced in this way. In the experiments, 3700 benchmark SAT problems in SATLIB are used to test the performance of OEA_SAT. The number of variables of these problems is ranged from 20 to 250. Moreover, the performance of OEA_SAT is compared with those of two well-known algorithms, namely WalkSAT and RFEA2. All experimental results show that OEA_SAT has a higher success ratio and a lower computational cost. OEA_SAT can solve the problems with 250 variables and 1065 clauses by only 1.524 seconds and outperforms all the other algorithms.Evolutionary Computation, 2009. CEC '09. IEEE Congress on; 06/2009