Abstract: RFID antennas are ubiquitous, so exploring the space of high efficiency and low resonant frequency antennas is an important multiobjective problem. Previous work has shown that the continuous solver differential evolution (DE) can be successfully applied to this discrete problem, but has difficulty exploring the region of solutions with lowest resonant frequency. This paper introduces a modified DE algorithm that uses biased selection from an archive of solutions to direct the search toward this region. Results indicate that the proposed approach produces superior attainment surfaces to the earlier work. The biased selection procedure is applicable to other population-based approaches for this problem.

Abstract: Local search is an integral part of many meta-heuristic strategies that solve single objective optimisation problems. Essentially, the meta-heuristic is responsible for generating a good starting point from which a greedy local search will find the local optimum. Indeed, the best known solutions to many hard problems (such as the travelling salesman problem) have been generated in this hybrid way. However, for multiple objective problems, explicit local search strategies are relatively rarely mentioned or applied. In this paper, a generic local search strategy is developed, particularly for problems where it is difficult or impossible to determine the contribution of individual solution components (often referred to as inseparable problems). The meta-heuristic adopted to test this is extremal optimisation, though the local search technique may be used by any meta-heuristic. To supplement the local search strategy a diversication strategy that draws from the external archive is incorporated into the local search strategy. Using benchmark problems, and a real-world airfoil design problem, it is shown that this combination leads to improved solutions.

Abstract: Interactive Multi-Objective Optimisation is an increasingly growing field of evolutionary and swarm intelligence-based algorithms. By involving a human decision a set of relevant non-dominated points can often be acquired at significantly lower computational costs than with \textit{a posteriori} algorithms. An often neglected issue in interactive optimisation is the issue of user interface design and the application of interactive optimisation as a design tool in engineering applications. This paper will discuss recent advances made in and moduli for an interactive multi-objective particle swarm optimisation algorithm. The focus of current implementation is on an aeronautics engineering applications, however, use of it for a wide range of other optimisation problems is conceivable.