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This paper considered the semivectorial bilevel programming problem. By using Benson's method and dual theory of linear programming, the original problem was transformed into a single level optimization problem. This paper gave a definition of partial calmness of the transformed problem. Based on this definition, this paper constructed an exact penalized problem of the semivectorial bilevel programming problem, gave its optimality condition, and proposed an algorithm to solve it. Finally, this paper gave a numerical example to show that the algorithm is feasible. [ABSTRACT FROM AUTHOR]

Swarm intelligent algorithms are derived from the simulation of natural evolution or collective behavior of animals to seek solutions of complicated optimization problems by exploring and exploiting the search space efficiently and effectively. Through the analysis on the search characteristics of swarm intelligent algorithms, the concept of solution set diversity is introduced in this paper according to the infrastructural changes of solution sets in search processes. Two categories of fundamental search strategies, i.e. the diversification search and the intensification search, are then defined and their influences on the stagnation of solution sets evolution are investigated on the basis of the solution set diversity. It is proved in this paper that an intensification strategy inevitably leads candidates solution to a single solution, which is one of the main sources of stagnation; while a diversification strategy is able to reach any point of the coding space from each initial candidate solution, i.e., the whole searching space is the reachable region of the diversification strategy, but the convergence of the algorithm cannot be guaranteed. Three popular swarm intelligent algorithms, i.e. the canonical generic algorithm, the ant colony system and the discrete particle swarm optimization, are tested with a benchmark problem, and the results support the theoretical conclusions. [ABSTRACT FROM AUTHOR]

This paper, based on the consideration of the handling time, aimed to conduct a study on the optimization of the batches processing modes and resource scheduling for the mixed-model manufacturing system. The time models of the processing and handling for batch parts were established in order to obtain the solutions to the makespan and optimize the handling frequency. In this foundation, a principle was analyzed for determining the optimum processing sequencing. Besides, an optimization algorithm of the mutual-aid teams was also designed to schedule the handling equipments needed by the optimum processing sequencing. The results show that the study in this thesis can provide effective solutions to the makespan of batch parts, optimize the handling time and frequency, acquire the optimum processing sequencing, and schedule the resources of the handling equipments sensibly. [ABSTRACT FROM AUTHOR]

As to the applied range and optimization of grey forecast model, according to the principle that grey GM(1,1) model's parameters are grey and adjustable, the connotation model and parameter packet formulas of GM(1,1, β) were put forward in this paper, and some properties of the model and model's optimization algorithm was analyzed. The results show that the objective valuing region of parameter a of GM(1,1, β) model is (-∞+ ∞), and the objective valuing region of parameter a of GM(1,1) model is (- 2,+2), the objective valuing region of parameter a is determined by the background value coefficient, and it has nothing with raw data sequence; GM(1,1, β) grey differential equation completely suits for the homogeneous exponential function series. Finally, the example of simulating and forecasting the per capita income of urban dwellers in China shows that the grey differential equation GM(1,1, β) is valid. [ABSTRACT FROM AUTHOR]

The paper discussed characteristics of Forman theory and presented an optimization model based on discrete Morse theory, which could obtain an optimal value or approximate optimal one by the algorithm of constructing a discrete Morse function in 3-dimensional space. It is a radical attempt. The experimental results show that this model is effective for applications and especially for optimization problem of a large amount of data. [ABSTRACT FROM AUTHOR]

Many real-world optimization problems are both dynamic and multi-modal, which require an optimization algorithm not only to find as many as possible optima under a specific environment but also to track their trajectory over dynamic environments. To address this requirement, this paper investigates a memetic particle swarm algorithm for dynamic multi-modal optimization problems. Within the framework of the proposed algorithm, a new speciation method is employed to locate multiple peaks and an adaptive local search method is also hybridized to accelerate the exploitation of species generated by the speciation method. In addition, the re-initialization schemes are introduced into the proposed algorithm in order to further enhance its performance in dynamic multi-modal environments. Based on the moving peaks benchmark problems, experiments are carried out to investigate the performance of the memetic particle swarm algorithm in comparison with several state-of-the-art algorithms in the literature. The experimental results show the efficiency of our proposed algorithm for dynamic multi-modal optimization problems. [ABSTRACT FROM AUTHOR]

Vehicle routing problem (VRP) plays a vital role in logistics research. The multi-depot vehicle routing problem with time windows (MDVRPTW), an extension of VRP with time windows (VRPTW), is very complicated to be solved. This paper presented a two-stage heuristic to solve MDVRPTW. In the two-stage heuristic, aggregation-based clustering algorithm was firstly proposed to transfer MDVRPTW to several VRPTWs, and ant colony optimization (ACO) was developed to optimize each VRPTW. Two improvement strategies: crossover operation and adaptive ant-weight strategy, are designed to improve ACO. The performance of the two-stage heuristic was examined by use of some classic instances. Results show that the two-stage heuristic is a powerful tool for MDVRPTW. [ABSTRACT FROM AUTHOR]

The paper took different regions and regional industries as initial water right allocation objects in the basin; the compound system for basin initial water right allocation was given; the connotation and objective of the compound system was analyzed; and the bi-level optimization model of the compound system optimization was established to allocate water right of different regions and regional industries, and eliminate the water conflict and contradiction among regions, embody the fairness and efficiency of different regions' water right, prevent different industries from the vicious water right competition, optimize comprehensive benefit of society-economy-ecology in the basin. Meanwhile, according to the interactive decision-making theory, the model algorithm was put forward. The case analysis verified the effectiveness of the model. [ABSTRACT FROM AUTHOR]

Considering that the global optimal solutions often locate on or near the boundary of the feasible region for many constrained optimization problems, a novel genetic algorithm was proposed in this paper. The basic idea of the proposed algorithm was to put feasible solutions and infeasible solutions into two different containers respectively. Subsequently, a new designed crossover operator (named inward-outward crossover operator) was used to a feasible solution and a infeasible solution, then a line search along a potential decent direction was used to improve the offspring so as to find a good solution on or near to the boundary of feasible region. By this search procedure, the possibility for obtaining the globally optimal solution is obviously enhanced, and similarly, the convergent speed is also strengthened. The "particle swarm mutation" inherited the advantages of Particle Swarm Optimization (PSO) algorithm and searched for the potential solution along the direction of the best current particle and the direction of the best individual of the whole swarm in the past. Selection operator retained a constant rate of infeasible solutions. Numerical results indicate that the proposed algorithm can be efficient to get global optimal solutions or near to them in smaller population and less iteration times. [ABSTRACT FROM AUTHOR]