Evolutionary multiobjective optimization has become a very popular topic in the last few years. Since the 1980s, various evolutionary approaches that are capable of searching for multiple solutions simultaneously in a single run have been developed to solve multiobjective optimization problems (MOPs). However, to find a uniformly distributed, near-complete, and near-optimal Pareto front in a sm...

In this paper, we demonstrate the benefits of using a multi-objective approach when tuning the parameters of an Evolutionary Algorithm. To overcome the specific challenges that arise when using a meta-algorithm for parameter tuning on multiple functions, we introduce a new algorithm called the Multi-Function Evolutionary Tuning Algorithm (M-FETA) that is able to approximate the parameter Pareto...

Multi-objective-optimization-based positioning of houses in a residential neighborhood is described. The task is the placement of the buildings in a favorable configuration constrained by two objectives, which are garden performance and visual privacy performance requirements. The method used is evolutionary computation with the Pareto front based on a weighted function of the objectives. It is...

In this paper, we develop a novel algorithm which finds a subset of Pareto front of a Multi-Objective Distributed Constraint Optimization Problem. This algorithm utilizes the Lp-norm method, pseudo-tree, and Dynamic Programming technique. Furthermore, we show that this Lp-norm based algorithm can only guarantee to find a Pareto optimal solution, when we employ L1-norm (Manhattan norm).

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 ...

This paper introduces a deterministic method for capturing the Pareto front in multi-objective real parameter optimization problems. It is an effort to deal with multi-objective optimization problems that were until now confronted only by stochastic algorithms, specifically evolutionary algorithms. The method is based on a theoretical result that proves the equivalence of non-dominated points t...

This paper describes and demonstrates a new and highly innovative technique that identifies an approximation of the entire bounding surface of the feasible objective region directly, including deep concavities, disconnected regions and the edges of interior holes in the feasible areas. The Pareto front is a subset of the surface of the objective boundary and can be extracted easily. Importantly...