Computational Optimisation in the Real World (CORW) Session 1

Time and Date: 11:00 - 12:40 on 12th June 2014

Room: Tully III

Chair: Timoleon Kipouros

276 Extending the Front: Designing RFID Antennas using Multiobjective Differential Evolution with Biased Population Selection [abstract]
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.
James Montgomery, Marcus Randall, Andrew Lewis
396 Local Search Enabled Extremal Optimisation for Continuous Inseparable Multi-objective Benchmark and Real-World Problems [abstract]
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.
Marcus Randall, Andrew Lewis, Jan Hettenhausen, Timoleon Kipouros
411 A Web-Based System for Visualisation-Driven Interactive Multi-Objective Optimisation [abstract]
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.
Jan Hettenhausen, Andrew Lewis, Timoleon Kipouros

Computational Optimisation in the Real World (CORW) Session 2

Time and Date: 14:10 - 15:50 on 12th June 2014

Room: Tully III

Chair: Andrew Lewis

92 A Hybrid Harmony Search Algorithm for Solving Dynamic Optimisation Problems [abstract]
Abstract: Many optimisation problems are dynamic in the sense that changes occur during the optimisation process, and therefore are more challenging than the stationary problems. The occurrences of such problems have attracted researchers into studying areas of artificial intelligence and operational research. To solve dynamic optimisation problems, the proposed approaches should not only attempt to seek the global optima but be able to also keep track of changes in the track record of landscape solutions. Population-based approaches have been intensively investigated to address these problems, as solutions are scattered over the entire search space and therefore helps in recognizing any changes that occur in the search space but however, optimisation algorithms that have been used to solve stationary problems cannot be directly applied to handle dynamic problems without any modifications such as in maintaining population diversity. In this research work, one of the most recent new population-based meta-heuristic optimisation technique called a harmony search algorithm for dynamic optimization problems is investigated. This technique mimics the musical process when a musician attempts to find a state of harmony. In order to cope with a dynamic behaviour, the proposed harmony search algorithm was hybridised with a (i) random immigrant, (ii) memory mechanism and (iii) memory based immigrant scheme. This hybridisation processes help to keep track of the changes and to maintain the population diversity. The performance of the proposed harmony search is verified by using the well-known dynamic test problem called the Moving Peak Benchmark (MPB) with a variety of peaks. The empirical results demonstrate that the proposed algorithm is able to obtain competitive results, but not the best for most of the cases, when compared to the best known results in the scientific literature published so far.
Ayad Turky, Salwani Abdullah, Nasser Sabar
313 Constraint Programming and Ant Colony System for the Component Deployment Problem [abstract]
Abstract: Contemporary motor vehicles have increasing numbers of automated functions to augment the safety and comfort of a car. The automotive industry has to incorporate increasing numbers of processing units in the structure of cars to run the software that provides these functionalities. The software components often need access to sensors or mechanical devices which they are designed to operate. The result is a network of hardware units which can accommodate a limited number of software programs, each of which has to be assigned to a hardware unit. A prime goal of this deployment problem is to nd softwareto-hardware assignments that maximise the reliability of the system. In doing so, the assignments have to observe a number of constraints to be viable. This includes limited memory of a hardware unit, collocation of software components on the same hardware units, and communication between software components. Since the problem consists of many constraints with a signicantly large search space, we investigate an ACO and constraint programming (CP) hybrid for this problem. We nd that despite the large number of constraints, ACO on its own is the most eective method providing good solutions by also exploring infeasible regions.
Dhananjay Thiruvady, I. Moser, Aldeida Aleti, Asef Nazari
416 Electrical Power Grid Network Optimisation by Evolutionary Computing [abstract]
Abstract: A major factor in the consideration of an electrical power network of the scale of a national grid is the calculation of power flow and in particular, optimal power flow. This paper considers such a network, in which distributed generation is used, and examines how the network can be optimized, in terms of transmission line capacity, in order to obtain optimal or at least high-performing configurations, using multi-objective optimisation by evolutionary computing methods.
John Oliver, Timoleon Kipouros, Mark Savill