An analytical model is developed to assign regional freight across a multimodal highway and railway network using geographic information systems. As part of the regional planning process, the model is an iterative procedure that assigns multimodal freight traffic to the network to achieve overall network optimum. The procedure integrates optimization programming with traffic assignment, conside...

Interest in multimodal function optimization is expanding rapidly since real-world optimization problems often require location of multiple optima in a search space. In this paper, we propose a novel genetic algorithm which combines crowding and clustering for multimodal function optimization, and analyze convergence properties of the algorithm. The crowding clustering genetic algorithm employs...

The optimization of multimodal functions is a challenging task, in particular when derivatives are not available for use. Recently, in a directional direct search framework, a clever multistart strategy was proposed for global derivative-free optimization of single objective functions. The goal of the current work is to generalize this approach to the computation of global Pareto fronts for mul...

In contrast to optimization techniques intended to find a single, global solution in a problem domain, niching (speciation) techniques have the ability to locate multiple solutions in multimodal domains. Numerous niching techniques have been proposed, broadly classified as temporal (locating solutions sequentially) and parallel (multiple solutions are found concurrently) techniques. Most resear...

Image registration (IR) is the process of geometric overlaying or alignment of two or more 2D/3D images of the same scene (unimodal registration), taken or not at different time slots, from different angles, and/or by different image acquisition systems (multimodal registration). Technically, image registration implies a complex optimization of different parameters, performed at local or/and gl...

This paper presents a new method, which combines sharing and a fuzzy clustering technique to improve the performance of genetic algorithms in multimodal function optimization. This approach permits some limitations of the traditional sharing scheme to be overcome. Without using any prior information, it allows both location and maintenance of niches. Computer simulations show good performance f...

This paper presents an improved particle swarm optimizer (PSO) for solving multimodal optimization problems with problem-specific constraints and mixed variables. The standard PSO is extended by employing a comprehensive learning strategy, different particle updating approaches, and a feasibility-based rule method. The experiment results show the algorithm located the global optima in all teste...