Yuhui Qiu

A new adaptive well-chosen inertia weight strategy to automatically harmonize global and local search ability in particle swarm optimization

The global search ability and local search ability are two highly important components of particle swarm optimizer, which are inconsistent each other in many cases, we proposed a novel inertia weight strategy that can adaptively select a preferable inertia weight decline curve for a particle swarm form curves of the constructed function according to the fitness value of swarm, and to automatically harmonize global and local search ability, quicken convergence speed, avoid premature problem, and obtain global optimum. Experimental results on several benchmark functions show that the algorithm can rapidly converge at very high quality solutions

Tabu search algorithm based on insertion method

This paper presents a tabu insertion search algorithm (TIS) based on the merits of insertion method (IM) and tabu search (TS) algorithm for solving travel salesman problem (TSP). TIS combines the good local search ability of tabu search with the good tour construct ability of insertion method to search the solution space and to solve the combinatorial optimization problem. In this paper, we use the classical NP hard problem TSP to test the effectiveness of TIS. Results show that TIS has the good ability to jump beyond local optimum and to obtain the global optimum.

A modified particle swarm optimizer with roulette selection operator

In this paper, a novel particle swarm optimizer combined with the roulette selection operator is proposed, which provides a mechanism to restrain the predominating of super particles in early stage and can effectively avoid the premature problem. We conduct variety experiments to test the proposed algorithm and compare it with other published methods on several test functions taken from the literature. The computational results demonstrate that this revised algorithm is promising to achieve faster convergence and better solutions, especially for multimodal function optimization.

A novel adaptive search strategy of intensification and diversification in tabu search

Intensification and diversification are two highly important components of tabu search. At many cases, these two components are also conflicting between them. It is worthy of being studied that how to harmonize this conflict. Aiming at this problem, a novel adaptive search strategy of intensification and diversification was proposed in this paper. Taking traveling salesman problem as samples, many experiments was investigated. The results shows: this strategy is feasible and effective.

A novel path planning for mobile robots using modified particle swarm optimizer

Path planning for mobile robots is an important topic in modern robotics. This paper proposes a novel approach to path planning problem for mobile robots, in which the model of the vertexes of obstacles is constructed to describe two-dimensional map of work place of the mobile robot in order to obtain a constrained path function from the start location to the goal location, and further translates the path planning problem into nonlinear constrained function minimum optimization problem, finally using modified particle swarm optimizer optimizes forcefully the function to get the satisfactory collision-free path solution. The effectiveness of the approach is demonstrated by simulation static and dynamic environment

A parallel adaptive tabu search approach for traveling salesman problems

TSP (traveling salesman problem) is one of the typical combinatorial optimization problems, which is NP-hard. It is widely believed that there is no efficient polynomial time algorithm that can solve it accurately. On the other hand, this problem is very important since it has many applications in practice. It has been verified that TS (tabu search) is one of the meta-heuristic algorithms that can solve this problem satisfied. With the requirement of solving large-scale problems, we presented a new parallel tabu search (PTS) approach, which was cooperated with genetic crossover operation, for TSPs. In addition, a novel adaptive search strategy of intensification and diversification in TS was proposed to improve the solving quality and efficiency. Through computational experiment, it is showed that our proposed PTS is feasible and effective.

Optimizing weights of neural network using an adaptive tabu search approach

Feed forward Neural Network (FNN) has been widely applied to many fields because of its ability to closely approximate unknown function to any degree of desired accuracy. Gradient techniques, for instance, Back Propagation (BP) algorithm, are the most general learning algorithms. Since these techniques are essentially local optimization algorithms, they are subject to converging at the local optimal solutions and thus perform poorly even on simple problems when forecasting out of samples. Consequently, we presented an adaptive Tabu Search (TS) approach as a possible alternative to the problematical BP algorithm, which included a novel adaptive search strategy of intensification and diversification that was used to improve the efficiency of the general TS. Taking the classical XOR problem and function approximation as examples, a compare investigation was implemented. The experiment results show that TS algorithm has obviously superior convergence rate and convergence precision compared with other BP algorithms.

Tuning Neuro-Fuzzy Function Approximator by Tabu Search

Gradient techniques and genetic algorithms are currently the most widely used parameters learning methods for fuzzy neural networks. Since Gradient techniques search for local solutions and GA is easy to premature, tabu search algorithms are currently being investigated for the development of adaptive or self-tuning neuro-fuzzy approximator(NFA). By using the globe search technique, the fuzzy inference rules are built automatically. To show the effectiveness of this methodology, it has been used for modeling static nonlinear systems.

An adaptive diversity strategy for particle swarm optimization

In this paper, we present a diversity strategy for particle swarm optimizer. The modified algorithm re-initializes part of particles with poorer fitness during the searching process. It is empirically tested and compared with other published methods on many famous benchmark functions. The experimental results illustrate that the proposed algorithm has the potential to achieve higher success ratio and better solution quality. It is very competitive for hard multimodal function optimization.

Research on influence of solving quality based on different initializing solution algorithm in tabu search

Many research results show that TS (tabu search or taboo search) is depended on the selection of initial solutions. For good initial solutions, TS can find better results with quicker speed in the solution space, but poor initial solutions may decrease the TS convergence speed. This paper investigates and compares three common initializing algorithms (greedy, insertion and randomization) to solve the TSP (traveling salesman problem). The experiment shows that if we pursue different solving qualities, and face different problem sizes, we should select different initializing algorithms to generate the initial solutions.