Multi objective optimization is a promising field which is increasingly being encountered in many areas worldwide. Various metaheuristic techniques such as differential evolution (DE), genetic algorithm (GA), gravitational search algorithm (GSA), and particle swarm optimization (PSO) have been used to solve Multi objective problems. Various multiobjective evolutionary algorithms have been devel...

Sensors used in image acquisition. This sensor technology is going on upgrading as per user need or of an application. Multiple sensors collect the information their respective wavelength band. But one not sufficient to acquire complete scene. To gain overall data part, it becomes essential cartel images from multiple sources. achieved through merging. It method merging dissimilar input sources...

In this paper, the problem under consideration is multiobjective non-linear fractional programming problem involving semilocally convex and related functions. We have discussed the interrelation between the solution sets involving properly efficient solutions of multiobjective fractional programming and corresponding scalar fractional programming problem. Necessary and sufficient optimality...

The multiple criteria aggregation methods allow us to construct a recommendation from a set of alternatives based on the preferences of a decision maker. In some approaches, the recommendation is immediately deduced from the preferences aggregation process. When the aggregation model of preferences is based on the outranking approach, a special treatment is required, but some non-rational viola...

Airline crew pairing problems involve assigning the required crew members to each flight segment in a given time period, while complying with a variety of work regulations and collective agreements. Traditional researches formulate the pairing problems as integer programming problems, and use deterministic approaches to optimize the solutions. Such these approaches usually suffer from some crit...

Many evolutionary algorithms have been lately developed for solving multiobjective problems, appealing or not to the Pareto optimality concept. Although, the evolutionary techniques for multiobjective optimization confront with several issues as: elitism, diversity of the population, or efficient settings for the specific parameters of the algorithm. In this paper, we propose a new evolutionary...