The alignment of biological sequences is a crucial tool in molecular biology and genome analysis. A wide variety of approaches has been proposed for multiple sequence alignment problem; however, some of them need prerequisites to help find the best alignment or some of them may suffer from the drawbacks of complexity and memory requirement so they can be only applied to cases with a limited number of sequences. In this paper, we view the multiple sequence alignment problem as an optimization problem and propose a heuristic-based genetic algorithm (GA) approach to solve it. The heuristic/GA hybrid yields better results than other well-known packages do. Experimental results are presented to illustrate the feasibility of the proposed approach.

The number and location of the inventory centers play an important role in the material distribution process since residents and inventory centers may be in dispersed regions. In this paper, we view the problem of finding the better locations for the inventory centers as an optimization problem, and propose a nested genetic algorithm (NGA) approach to design an optimal material distribution system. We demonstrate the feasibility of the proposed approach by numerical experiments.

Image segmentation denotes a process by which an image is partitioned into non-intersecting regions and each region is homogeneous. Utilizing histogram information to aim at segmenting an image is a commonly used method for many applications. In this paper, we view the image segmentation as an optimization problem. We find a curve which gives the best fit to the given image histogram, and the parameters in the curve are determined by using the particle swarm optimization algorithm. The experimental results to confirm the proposed approach are also included.