Yali Wu

Brain storm optimization algorithm for multi-objective optimization problems

In this paper, a novel multi-objective optimization algorithm based on the brainstorming process is proposed(MOBSO). In addition to the operations used in the traditional multi-objective optimization algorithm, a clustering strategy is adopted in the objective space. Two typical mutation operators, Gaussian mutation and Cauchy mutation, are utilized in the generation process independently and their performances are compared. A group of multi-objective problems with different characteristics were tested to validate the effectiveness of the proposed algorithm. Experimental results show that MOBSO is a very promising algorithm for solving multi-objective optimization problems.

Multi-Objective Optimization Based on Brain Storm Optimization Algorithm

In recent years, many evolutionary algorithms and population-based algorithms have been developed for solving multi-objective optimization problems. In this paper, the authors propose a new multi-objective brain storm optimization algorithm in which the clustering strategy is applied in the objective space instead of in the solution space in the original brain storm optimization algorithm for solving single objective optimization problems. Two versions of multi-objective brain storm optimization algorithm with different characteristics of diverging operation were tested to validate the usefulness and effectiveness of the proposed algorithm. Experimental results show that the proposed multi-objective brain storm optimization algorithm is a very promising algorithm, at least for solving these tested multi-objective optimization problems.

Modified Brain Storm Optimization Algorithm for Multimodal Optimization

Multimodal optimization is one of the most challenging tasks for optimization. The difference between multimodal optimization and single objective optimization problem is that the former needs to find both multiple global and local optima at the same time. A novel swarm intelligent method, Self-adaptive Brain Storm Optimization (SBSO) algorithm, is proposed to solve multimodal optimization problems in this paper. In order to obtain potential multiple global and local optima, a max-fitness grouping cluster method is used to divide the ideas into different sub-groups. And different sub-groups can help to find the different optima during the search process. Moreover, the self-adaptive parameter control is applied to adjust the exploration and exploitation of the proposed algorithm. Several multimodal benchmark functions are used to evaluate the effectiveness and efficiency. Compared with the other competing algorithms reported in the literature, the new algorithm can provide better solutions and show good performance.

A Modified Multi-Objective Optimization Based on Brain Storm Optimization Algorithm

In recent years, many evolutionary algorithms and population-based algorithms have been developed for solving multi-objective optimization problems. In this paper, A new Multi-objective optimization algorithm-Modified Multiobjective Brain Storm Optimization (MMBSO) algorithm is proposed. The clustering strategy acts directly in the objective space instead of in the solution space and suggests potential Pareto-dominance areas in the next iteration. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise (DBSCAN) clustering and Differential Evolution (DE) mutations are used to improve the performance of MBSO. A group of multi-objective problems with different characteristics were tested to validate the usefulness and effectiveness of the proposed algorithm. Experimental results show that MMBSO is a very promising algorithm for solving these tested multi-objective problems.

Modeling and simulation of hybrid dynamical systems with generalized differential Petri nets

There has been considerable interest in the modeling and simulation of hybrid dynamic systems (HDS). As an effective modeling tool of discrete event dynamic systems, Petri nets have a dual nature of a graphical tool and a mathematical object. In this paper, an extension of Petri net named generalized differential Petri nets (GDPN) is proposed to model HDS that have a combination of discrete-event evolution and continuous state evolution. Differential place and differential transition defined by Demongodin et al. (1998) are used to model the numeral simulation of the continuous dynamic process, and the weights defined on the directed arcs that connects the differential transitions and places are extended from real numbers to real matrices. The marking of differential place are also enlarged from real numbers to real vectors. Then the evolution rules and modeling method of HDS with GDPN are introduced. Two examples are used to illustrate the modeling power of GDPN in HDS.

An Adaptive Brain Storm Optimization Algorithm for Multiobjective Optimization Problems

Brain Storm Optimization BSO algorithm is a new swarm intelligence method that arising from the process of human beings problem-solving. It has been well validated and applied in solving the single objective problem. In order to extend the wide applications of BSO algorithm, a modified Self-adaptive Multiobjective Brain Storm Optimization SMOBSO algorithm is proposed in this paper. Instead of the

Hybrid Petri nets of scheduling and supervisory control integrated system in process industry

k

New matrix method for analyzing steel making process

k-means clustering of the traditional algorithm, the algorithm adopts the simple clustering operation to increase the searching speed. At the same time, the open probability is introduced to avoid the algorithm trapping into local optimum, and an adaptive mutation method is used to give an uneven distribution on solutions. The proposed algorithm is tested on five benchmark functions; and the simulation results showed that the modified algorithm increase the diversity as well as the convergence successfully. The conclusions could be made that the SMOBSO algorithm is an effective BSO variant for multiobjective optimization problems.