The following paper describes a cooperative coevolutionary algorithm which incorporates a novel collaboration formation mechanism. It encourages rewarding of components participating in successful collaborations from each sub-population. The successfulness of the collaboration is measured by a non-dominated sorting procedure. The algorithm has demonstrated it can perform comparably with the NSG...

This paper introduces a modified PSO, Non-dominated Sorting Particle Swarm Optimizer (NSPSO), for better multiobjective optimization. NSPSO extends the basic form of PSO by making a better use of particles’ personal bests and offspring for more effective nondomination comparisons. Instead of a single comparison between a particle’s personal best and its offspring, NSPSO compares all particles’ ...

The growing interest in hard multiple objective combinatorial and non-linear problems resulted in a significant number of heuristic methods aiming at generating sets of feasible solutions as approximations to the set of non-dominated solutions. The issue of evaluating these approximations is addressed. Such evaluations are useful when performing experimental comparisons of different multiple ob...

Modern domain-independent heuristic planners evaluate their plans on the basis of their length only. However, in real-world problems there are other criteria that also play an important role, such as resource consumption, profit, safety, etc. This paper extends the GRT planner, an efficient domain-independent heuristic state-space planner, with the ability to consider multiple criteria. The heu...

Using diversity mechanisms in evolutionary algorithms for multi-objective optimization problems is considered as an important issue for the design of successful algorithms. This is in particular the case for problems where the number of non-dominated feasible objective vectors is exponential with respect to the problem size. In this case the goal is to compute a good approximation of the Pareto...

Using diversity mechanisms in evolutionary algorithms for multi-objective optimization problems is considered as an important issue for the design of successful algorithms. This is in particular the case for problems where the number of non-dominated feasible objective vectors is exponential with respect to the problem size. In this case the goal is to compute a good approximation of the Pareto...

Today major section of automatic speaker verification (ASV) research is focused on multiple objectives like optimization of feature subset and minimization of Equal Error Rate (EER). As such, numerous systems for feature dimension reduction are proposed. This includes framework coaching and testing analysis for every feature set that could be a time esurient trip. Because of its significance, t...

This paper describes algorithms for generating shapes of side actions to minimize a customizable injection molding cost function. Given a set of undercut facets on a polyhedral part and the main mold opening directions, our approach works in the following manner. First, we compute candidate retraction space for every undercut facet. This space represents the set of candidate translation vectors...

A novel multiobjective optimisation accelerator is introduced that uses direct manipulation in objective space together with neural network mappings from objective space to decision space. This operator is a portable component that can be hybridized with any multiobjective optimisation algorithm. The purpose of this Convergence Acceleration Operator (CAO) is to enhance the search capability and...