Evolutionary algorithms have been recognized to be suitable for extracting approximate solutions of multi-objective problems because of their capability to evolve a set of non-dominated solutions distributed along the Pareto frontier‎. ‎This paper applies an evolutionary optimization scheme‎, ‎inspired by Multi-objective Invasive Weed Optimization (MOIWO) and Non-dominated Sorting (NS) strategi...

In spite of the recent quick growth of the Evolutionary Multi-objective Optimization (EMO) research field, there has been few trials to adapt the general variation operators to the particular context of the quest for the Pareto-optimal set. The only exceptions are some mating restrictions that take in account the distance between the potential mates – but contradictory conclusions have been rep...

Decomposition-based methods are often cited as the solution to multi-objective nonconvex optimization problems with an increased number of objectives. These methods employ a scalarizing function to reduce the multi-objective problem into a set of single objective problems, which upon solution yield a good approximation of the set of optimal solutions. This set is commonly referred to as Pareto ...

Multi-objective problems are a category of optimization problem that contains more than one objective function and these objective functions must be optimized simultaneously. Should the objective functions be conflicting, then a set of solutions instead of a single solution is required. This set is known as Pareto optimal. Multi-objective optimization problems arise in many real world applicati...

A single-point local search method is presented as a simplification of the multiobjective tabu search. Some improvements are made to reach the Pareto front within small number of function evaluations. The performance of the proposed method is first verified by a small mathematical problem. It is shown that accurate Pareto optimal solutions with good diversity are obtained by using the proposed ...

Multiple objective optimization involves the simultaneous optimization of several objective functions. Solving this type of problem involves two stages; the optimization stage and the post-Pareto analysis stage. The first stage focuses in obtaining a set of nondominated solutions while the second one involves the selection of one solution from the Pareto set. Most of the work found in the liter...

This work studies the behavior of three elitist multiand many-objective evolutionary algorithms generating a high-resolution approximation of the Pareto optimal set. Several search-assessment indicators are defined to trace the dynamics of survival selection and measure the ability to simultaneously keep optimal solutions and discover new ones under different population sizes, set as a fraction...

We address the problem of multi-objective constraint optimization problems (MO-COPs). Solving an MO-COP traditionally consists in computing the set of all Pareto solutions (i.e. the Pareto front). But this Pareto front is exponentially large in the general case. So this causes two main problems. First is the time complexity concerns. Second is a lack of decisiveness. In this paper, we present t...

The use of evolutionary strategies (ESs) to solve problems with multiple objectives (known as Vector Optimization Problems (VOPs)) has attracted much attention recently. Being population based approaches, ESs offer a means to find a set of Pareto-optimal solutions in a single run. Differential Evolution (DE) is an ES that was developed to handle optimization problems over continuous domains. Th...

This paper proposes a novel approach to generate a uniform distribution of the optimal solutions along the Pareto frontier. We make use of a standard mathematical technique for optimization namely line search and adapt it so that it will be able to generate a set of solutions uniform distributed along the Pareto front. To validate the method, numerical bi-criteria examples are considered. The m...