A core challenge ofMultiobjective Evolutionary Algorithms (MOEAs) is to attain evenly distributed Pareto optimal solutions along the Pareto front. In this paper, we propose a novel asymmetric Pareto-adaptive (apa) scheme for the identification of well distributed Pareto optimal solutions based on the geometrical characteristics of the Pareto front. The apa scheme applies to problem with symmetr...

A Multiobjective optimization approach is used to propose a set of digital controllers for the throttle control benchmark that fulfills its specifications and requirements. The Multiobjective approach allows us to determine the best set of solutions that characterizes the Pareto set, where each solution has a different trade-off between the design objectives.

In this paper, a procedure has been introduced to the multi-objective optimal design of semi-active tuned mass dampers (SATMDs) with variable stiffness for nonlinear structures considering soil-structure interaction under multiple earthquakes. Three bi-objective optimization problems have been defined by considering the mean of maximum inter-story drift as safety criterion of structural compone...

We deal with the problem of minimizing the expectation of a real valued random function over the weakly Pareto or Pareto set associated with a Stochastic MultiObjective Optimization Problem (SMOP) whose objectives are expectations of random functions. Assuming that the closed form of these expectations is difficult to obtain, we apply the Sample Average Approximation method (SAA-N, where N is t...

This paper presents an application of an evolutionary optimization algorithm for multiobjective analysis of selective withdrawal from a thermally stratified reservoir. A multiobjective particle swarm optimization (MOPSO) algorithm is used to find nondominated (Pareto) solutions when minimizing deviations from outflow water quality targets of: (i) temperature; (ii) dissolved oxygen (DO); (iii) t...

In ordinal regression, a score function and threshold values are sought to classify a set of objects into a set of ranked classes. Classifying an individual in a class with higher (respectively lower) rank than its actual rank is called an upgrading (respectively downgrading) error. Since upgrading and downgrading errors may not have the same importance, they should be considered as two differe...

We present results from a study comparing a recently developed coevolutionary genetic algorithm (CGA) against a set of evolutionary algorithms using a suite of multiobjective optimization benchmarks. The CGA embodies competitive coevolution and employs a simple, straightforward target population representation and fitness calculation based on developmental theory of learning. Because of these p...

In this paper, we propose new depth-first heuristic search algorithms to approximate the set of Pareto optimal solutions in multi-objective constraint optimization. Our approach builds upon recent advances in multi-objective heuristic search over weighted AND/OR search spaces and uses an -dominance relation between cost vectors to significantly reduce the set of non-dominated solutions. Our emp...

In this article, we present a method combining a genetic approach with a local search for multiobjective problems. It is an extension of algorithms for the single objective case, with specific mechanisms used to build the Pareto set. The performance of the proposed algorithm is illustrated by experimental results based on a real problem with three objectives. The problem is issued from electric...