An efficient algorithm ESA combining evolution strategies(ES) with simulated annealing(SA) is proposed in this paper. We first use ES to choose an initial temperature, then use a modified SA to find a global optimum f...An efficient algorithm ESA combining evolution strategies(ES) with simulated annealing(SA) is proposed in this paper. We first use ES to choose an initial temperature, then use a modified SA to find a global optimum for the problem. An efficient load flow method and a heuristic criterion for determining the temperature lowering scheme are employed in order to speed up the computation. The solution algorithm has been tested on a distribution system with very promising results.展开更多
The evolutionary algorithm, a subset of computational intelligence techniques, is a generic population-based stochastic optimization algorithm which uses a mechanism motivated by biological concepts. Bio-inspired comp...The evolutionary algorithm, a subset of computational intelligence techniques, is a generic population-based stochastic optimization algorithm which uses a mechanism motivated by biological concepts. Bio-inspired computing can implement successful optimization methods and adaptation approaches, which are inspired by the natural evolution and collective behavior observed in species, respectively. Although all the meta-heuristic algorithms have different inspirational sources, their objective is to find the optimum(minimum or maximum), which is problem-specific. We propose and evaluate a novel synergistic fibroblast optimization(SFO) algorithm, which exhibits the behavior of a fibroblast cellular organism in the dermal wound-healing process. Various characteristics of benchmark suites are applied to validate the robustness, reliability, generalization, and comprehensibility of SFO in diverse and complex situations. The encouraging results suggest that the collaborative and self-adaptive behaviors of fibroblasts have intellectually found the optimum solution with several different features that can improve the effectiveness of optimization strategies for solving non-linear complicated problems.展开更多
文摘An efficient algorithm ESA combining evolution strategies(ES) with simulated annealing(SA) is proposed in this paper. We first use ES to choose an initial temperature, then use a modified SA to find a global optimum for the problem. An efficient load flow method and a heuristic criterion for determining the temperature lowering scheme are employed in order to speed up the computation. The solution algorithm has been tested on a distribution system with very promising results.
文摘The evolutionary algorithm, a subset of computational intelligence techniques, is a generic population-based stochastic optimization algorithm which uses a mechanism motivated by biological concepts. Bio-inspired computing can implement successful optimization methods and adaptation approaches, which are inspired by the natural evolution and collective behavior observed in species, respectively. Although all the meta-heuristic algorithms have different inspirational sources, their objective is to find the optimum(minimum or maximum), which is problem-specific. We propose and evaluate a novel synergistic fibroblast optimization(SFO) algorithm, which exhibits the behavior of a fibroblast cellular organism in the dermal wound-healing process. Various characteristics of benchmark suites are applied to validate the robustness, reliability, generalization, and comprehensibility of SFO in diverse and complex situations. The encouraging results suggest that the collaborative and self-adaptive behaviors of fibroblasts have intellectually found the optimum solution with several different features that can improve the effectiveness of optimization strategies for solving non-linear complicated problems.
