MVMOO: Mixed variable multi-objective optimisation

Variable Dependence Interaction And Multi-objective Optimisation

Interaction among decision variables is inherent to a number of real-life engineering design optimisation problems. There are two types of interaction that can exist among decision variables: inseparable function interaction and variable dependence. The aim of this paper is to propose an Evolutionary Computing (EC) technique for handling variable dependence in multi-objective optimisation probl...

Randomised Variable Neighbourhood Search for Multi Objective Optimisation

Various local search approaches have recently been applied to machine scheduling problems under multiple objectives. Their foremost consideration is the identification of the set of Pareto optimal alternatives. An important aspect of successfully solving these problems lies in the definition of an appropriate neighbourhood structure. Unclear in this context remains, how interdependencies within...

Multi-objective Binary Search Optimisation

Multi-objective particle swarm optimisation methods

This study compares a number of selection regimes for the choosing of global best (gbest) and personal best (pbest) for swarm members in multi-objective particle swarm optimisation (MOPSO). Two distinct gbest selection techniques are shown to exist in the literature, those that do not restrict the selection of archive members and those with ‘distance’ based gbest selection techniques. Theoretic...

Multi-objective Optimisation Based on Relation Favour

Many optimisation problems in circuit design, in the following also refereed to as VLSI CAD, consist of mutually dependent sub-problems, where the resulting solutions must satisfy several requirements. Recently, a new model for Multi-Objective Optimisation (MOO) for applications in Evolutionary Algorithms (EAs) has been proposed. The search space is partitioned into socalled Satisfiability Clas...