Optimization via game theoretic control

Summary Using game theoretic control to solve optimization problem is a recently developed promising method. The key technique is to convert a networked system into a potential game, with a pre-assigned criterion as the potential function. An algorithm is designed for updating strategies to reach a Nash equilibrium (i.e. optimal solution).

A Multiple Mitosis Genetic Algorithm

<p>Genetic algorithm is a well-known metaheuristic method to solve optimization problem mimic the natural process of cell reproduction. Having great advantages on solving optimization problem makes this method popular among researchers to improve the performance of simple Genetic Algorithm and apply it in many areas. However, Genetic Algorithm has its own weakness of less diversity which cause premature convergence where the potential answer trapped in its local optimum. This paper proposed a method Multiple Mitosis Genetic Algorithm to improve the performance of simple Genetic Algorithm to promote high diversity of high-quality individuals by having 3 different steps which are set multiplying factor before the crossover process, conduct multiple mitosis crossover and introduce mini loop in each generation. Results shows that the percentage of great quality individuals improve until 90 percent of total population to find the global optimum.</p>

Stocks Allocation in Portfolio Selection using Fuzzy Soft Set

Return and risk are uncertain parameters for stock market. Fuzzy Soft Set is a suitable approach to handle the uncertaintiesvagueness and/or imprecisionof the market position and permits the data representation viably. The primary focus of paper is to construct the diversified portfolio of the stocks with the help of Fuzzy Soft Set (FSS) model.HereinFSS model is used for ranking the stocks viadecision making factor (DMF) and decision ranking factor (DRF).On the basis of this ranking7 stocks are picked up out of 30 stocks for construction of optimal portfolio. To solve optimization problem, Genetic Algorithm isused for stocks allocation of the optimal portfolio. The data set analysedin this model is taken from Bombay Stock Exchange (BSE) Mumbai, India and a real application are given in order to show the potentiality of the approach

A Combined Algorithm Based on ELM-RBF and Genetic Algorithm

Extreme Learning Machine-Radial Basis Function (ELM-RBF) not only inherit RBF’s merit of not suffering from local minima, but also ELM’s merit of fast learning speed, Nevertheless, it is still a research hot area of how to improve the generalization ability of ELM-RBF network. Genetic Algorithms (GA) to solve optimization problem has its unique advantage. Considered on these, the paper adopted GA to optimize ELM-RBF neural network hidden layer neurons center and biases value. Experiments data results indicated that our proposed combined algorithm has better generalization performance than classical ELM-RBF, it achieved the basic anticipated task of design.

Optimization Problem of Maintenance Resources Allocation Based on Partical Swarm Optimization

In the communications maintenance support system, how to progress the optimal allocation of maintenance resources is key to design the system. For Optimization Problem of Maintenance Resources Allocation, this paper analysis the phenomenon of premature convergence in Partical Swarm Optimization(PSO), and proposed an improved Particle Swarm Optimization. The two examples study how to use the improved PSO to solve Optimization Problem of Maintenance Resources and validate an improved PSO.

The Application of Genetic Algorithm on Optimization Problem for Mayonnaise Compositions

For some function optimization problems of non-linear, multi-model and multi-objective, they are difficult to solve by other optimization methods, however, genetic algorithm is easy to find good results, so a kind of optimization problem for mayonnaise compositions based on genetic algorithm is introduced. This termination condition is selected according to the iteration number of maximum generation, the optimal solution of last generation in the evolution is the final result with genetic algorithm to solve optimization problem. The population size is 20, crossover rate is 0.7, and mutation rate is 0.04. Via the evolution of 100 generations, the optimization solution is gotten, which has certain guiding significance for the production.