A chaotic search method for global optimization on tent map;

Back propagation algorithm of neural network with global optimization;

Numerical results show that the present algorithm is a promising approach for solving global optimization problems.

But BP arithmetic has a low identifying speed and easy to encounter local optimization.

The object of the optimization about some practice problem is that search for all local optimization value.

An improved hybrid genetic algorithm(IHGA)composed of the self-adaptive genetic algorithm(SAGA)based on neighborhood searching and the SGA(standard genetic algorithm)can bring into play their advantages of time saving,high efficiency and mature convergence,as well as the global optimum searching capability of GA(genetic algorithm).

Virtually all control and decision problems can be cast as optimization problems ,while most of them exist multiple minima ,so it is always a focus to achieve global optimum by escaping local minimum both in academic and engineering fields.

The hybrid method is an effective approach to solve nonconvex optimization problems, because it inherits the merits of fast searching ability to locate global optimum in the chaos optimization method and the advantage of high convergence speed of the BFGS method.

Particle swarm optimization algorithm is a swarm intelligence algorithm,which is easily trapped in local optima.

Implied by its three-term structure,the inherent shortcoming that trends to local optima is indicated.

The algorithm can generate an optimal local path for obstacle avoidance more efficiently in real time in the dynamic environments, prevent local optimum, and overcome the problem that there is no passage between closely spaced obstacles caused by directly applying the conventional artificial potential field method.

It overcomes the shortcomings of local optimum in some local path planner, like artificial potential field method.

To solve the problem of the number of coalition structure increasing rapidly, OCS algorithm—formation of agent coalition structure based on local optimum is given.