Genetic Algorithm For Global Optimization Research Articles

A niche hybrid genetic algorithm for global optimization of continuous multimodal functions

A niche hybrid genetic algorithm (NHGA) is proposed in this paper to solve continuous multimodal optimization problems more efficiently, accurately and reliably. It provides a new architecture of hybrid algorithms, which organically merges the niche techniques and Nelder–Mead's simplex method into GAs. In the new architecture, the simplex search is first performed in the potential niches, which likely contain a global optimum, to locate the promising zones within search space, quickly and reliably. Then another simplex search is used to quickly discover the global optimum in the located promising zones. The proposed method not only makes the exploration capabilities of GAs stronger through niche techniques, but also has more powerful exploitation capabilities by using simplex search. So it effectively alleviates premature convergence and improves weak exploitation capacities of GAs. A set of benchmark functions is used to demonstrate the validity of NHGA and the role of every component of NHGA. Numerical experiments show that the NHGA may, efficiently and reliably, obtain a more accurate global optimum for the complex and high-dimension multimodal optimization problems. It also demonstrates that the new hybrid architecture is potential and can be used to generate more potential hybrid algorithms.

An Improved Genetic Algorithm for Global Optimization and Its Application to Sodium Chloride Clusters

We have developed an improved genetic algorithm with a self-guiding search strategy, using a combination of “traditional” and geometric genetic operators, whose relative weights are determined by the algorithm itself. Explicit mutation becomes unnecessary, because randomness is included in some of the geometric operators. This still quite simple algorithm is applied to the search for global minima on the potential energy hyper-surface of the most widely studied ionic clusters, sodium chloride clusters, with up to 100 ions. We present 32 previously unpublished apparent global minima of the Coulomb + Born−Meyer potential in the Tosi Fumi parametrization. Comparison with already published minima of the shell model due to Rittner shows no structural differences. We are able to reproduce the experimental sodium chloride bulk energy quite closely. Therefore, the Coulomb + Born−Meyer potential appears to be a good approximation for neutral clusters of all sizes.

An improved genetic algorithm for global optimization of electromagnetic problems

An improved genetic algorithm (GA) for design optimization of electromagnetic devices is developed in this paper. Numerical test is done on a classical mathematical problem, and it shows that the proposed method has noticeable advantages over standard GAs. To demonstrate its applicability in engineering optimal design problems, the proposed algorithm, together with the charge simulation method, is used to solve the optimization of a bar-plane electrode model.

A new hybrid genetic algorithm for global optimization

A new hybrid genetic algorithm for global optimization