This work deals with multiobjective optimization problems using Genetic Algorithms (GA). A MultiObjective GA (MOGA) is proposed to solve multiobjective problems combining both continuous and discrete variables. This kind of problem is commonly found in chemical engineering since process design and operability involve structural and decisional choices as well as the determination of operating co...

A halftoning technique that uses a simple GA has proven to be very effective to generate high quality halftone images. Recently, the two major drawbacks of this conventional halftoning technique with GAs, i.e. it uses a substantial amount of computer memory and processing time, have been overcome by using an improved GA (GA-SRM) that applies genetic operators in parallel putting them in a coope...

Robust design optimization (RDO) problems can generally be formulated by incorporating uncertainty into the corresponding deterministic problems. In this context, a careful formulation of deterministic equality constraints into the robust domain is necessary to avoid infeasible designs under uncertain conditions. The challenge of formulating equality constraints is compounded in multiobjective ...

In this paper we systematically study the importance, i.e., the influence on performance, of the main design elements that differentiate scalarizing functions-based multiobjective evolutionary algorithms (MOEAs). This class of MOEAs includes Multiobjecitve Genetic Local Search (MOGLS) and Multiobjective Evolutionary Algorithm Based on Decomposition (MOEA/D) and proved to be very successful in m...

This paper describes the application of multiobjective heuristic search algorithms to the problem of hazardous material (hazmat) transportation. The selection of optimal routes inherently involves the consideration of multiple conflicting objectives. These include the minimization of risk (e.g. the exposure of the population to hazardous substances in case of accident), transportation cost, tim...

This chapter will focus on the multiobjective formulation of an optimization problem and highlight the assets of a multiobjective Tabu implementation for such problems. An illustration of a specific Multiobjective Tabu heuristic (referred to as MO Tabu in the following) will be given for 2 particular problems arising in wireless systems. The first problem addresses the planning of access points...

The relationship between mathematical programming and classical calculus of variation was explored and extended by Hanson [6]. Thereafter variational programming problems have attracted some attention in literature. Duality for multiobjective variational problems has been of much interest in the recent years, and several contributions have been made to its development (see, e.g., Bector and Hus...

The 0/1 knapsack problem is a well-known problem, which appears in many real domains with practical importance. The problem is NP-complete. The multiobjective 0/1 knapsack problem is a generalization of the 0/1 knapsack problem in which multiple knapsacks are considered. Many algorithms have been proposed in the past five decades for both single and multiobjective knapsack problems. A new versi...

This paper presents an evolutionary algorithm that incorporates a multilevel pairing strategy to solve single and multiobjective optimization problems. The algorithm is based on nondominance of solutions separately in the objective and the constraint space and uses cooperative mating strategies between solutions. Since the methodology is based on nondominance separately in the objective and the...