Neil B Urquhart

State assignment for sequential circuits using multi-objective genetic algorithm

In this study, a new approach using a multi-objective genetic algorithm (MOGA) is proposed to determine the optimal state assignment with less area and power dissipations for completely and incompletely specified sequential circuits. The goal is to find the best assignments which reduce the component count and switching activity. The MOGA employs a Pareto ranking scheme and produces a set of state assignments, which are optimal in both objectives. The ESPRESSO tool is used to optimise the combinational parts of the sequential circuits. Experimental results are given using a personal computer with an Intel CPU of 2.4 GHz and 2 GB RAM. The algorithm is implemented using C++ and fully tested with benchmark examples. The experimental results show that saving in components and switching activity are achieved in most of the benchmarks tested compared with recent published research.

Influence of topology and payload on CO 2 optimised vehicle routing

This paper investigates the influence of gradient and payload correction factors used within a CO2 emission model on the solutions to shortest path and travelling salesman problems when applied to freight delivery. Problem instances based on real life examples using the road network of Scotland are studied. Solutions are obtained using a range of metrics and vehicles. The results are compared to determine if the inclusion of gradient and payload as inputs to the emission model have any influence on the final routes taken by vehicles or the order of visiting customers. For the problem instances studied no significant influence was found. However for vehicle routing problems with large differences in payload and hilly road networks further investigation is needed.

Building low CO 2 solutions to the vehicle routing problem with Time Windows using an evolutionary algorithm

An evolutionary Multi-Objective Algorithm (MOA) is used to investigate the trade-off between CO2 savings, distance and number of vehicles used in a typical vehicle routing problem with Time Windows (VRPTW). A problem set is derived containing three problems based on accurate geographical data which encapsulates the topology of streets as well as layouts and characteristics of junctions. This is combined with realistic speed-flow data associated with road-classes and a power-based instantaneous fuel consumption model to calculate CO2 emissions, taking account of drive-cycles. Results obtained using a well-known MOA with twin objectives show that it is possible to save up to 10% CO2, depending on the problem instance and ranking criterion used.

Manipulation and optimisation techniques for Boolean logic

In this study, new techniques and algorithms are presented for the derivation and optimisation of mixed polarity Reed Muller (MPRM) and mixed polarity dual Reed Muller (MPDRM) functions. The first algorithm is used for bidirectional conversion between fixed polarity dual Reed Muller (FPDRM) and MPDRM and to derive any polarity from another polarity. The second algorithm is used to generate reduced MPDRM expressions from FPDRM using a new procedure based on tabular techniques. The third algorithm is proposed for bidirectional conversion between sum of products (SOP)/product of sums (POS) and MPRM/MPDRM forms, respectively. It can also be used to derive any mixed polarity from another MPRM/MPDRM. The last algorithm is to find optimal MPRM/MPDRM among 3n different polarities using genetic algorithm (GA) for large functions but without generating all the polarity sets. The proposed algorithms are efficient in terms of memory size and CPU time and can be used for large functions. Experimental results are given using a personal computer with an Intel CPU of 2.4 GHz and 2 GB RAM. All algorithms are implemented using C and fully tested with benchmark examples.

Using an evolutionary algorithm to discover low CO 2 tours within a travelling salesman problem

This paper examines the issues surrounding the effects of using vehicle emissions as the fitness criteria when solving routing problems using evolutionary techniques. The case-study examined is that of the Travelling Salesman Problem (TSP) based upon the road network within the City of Edinburgh, Scotland. A low cost path finding algorithm (A*) is used to build paths through the street network between delivery points. The EA is used to discover tours that utilise paths with low emissions characteristics. Two methods of estimating CO2 emissions are examined; one that utilises a fuel consumption model and applies it to an estimated drive cycle and one that applies a simplistic CO2 calculation model that focuses on average speeds over street sections. The results of these two metrics are compared with each other and with results obtained using a traditional distance metric.

Phase transition properties of clustered travelling salesman problem instances generated with evolutionary computation

This paper introduces a generator that creates problem instances for the Euclidean symmetric travelling salesman problem. To fit real world problems, we look at maps consisting of clustered nodes. Uniform random sampling methods do not result in maps where the nodes are spread out to form identifiable clusters. To improve upon this, we propose an evolutionary algorithm that uses the layout of nodes on a map as its genotype. By optimising the spread until a set of constraints is satisfied, we are able to produce better clustered maps, in a more robust way. When varying the number of clusters in these maps and, when solving the Euclidean symmetric travelling salesman person using Chained Lin-Kernighan, we observe a phase transition in the form of an easy-hard-easy pattern.

Automated Scheduling and Planning

Solving scheduling problems has long presented a challenge for computer scientists and operations researchers. The field continues to expand as researchers and practitioners examine ever more challenging problems and develop automated methods capable of solving them. This book provides 11 case studies in automated scheduling, submitted by leading researchers from across the world. Each case study examines a challenging real-world problem by analysing the problem in detail before investigating how the problem may be solved using state of the art techniques. The areas covered include aircraft scheduling, microprocessor instruction scheduling, sports fixture scheduling, exam scheduling, personnel scheduling and production scheduling. Problem solving methodologies covered include exact as well as (meta)heuristic approaches, such as local search techniques, linear programming, genetic algorithms and ant colony optimisation. The field of automated scheduling has the potential to impact many aspects of our lives and work; this book highlights contributions to the field by world class researchers.

Improving Street Based Routing Using Building Block Mutations

Street based routing (SBR) is a real-world inspired routing problem that builds routes within an urban area for mail deliveries. The authors have previously attempted to solve this problem using an Evolutionary Algorithm (EA). In this paper the authors examine a heuristic mutation based on concept of building blocks. In this case a building block is defined as a group of genes, which when placed together within a genotype result in a useful feature within the phenotype. After evaluation on three test data sets our experiments conclude that the explicit use of heuristic building blocks makes a significant improvement to the SBR algorithms results.

Multi-Modal Employee Routing with Time Windows in an Urban Environment

An urban environment provides a number of challenges and opportunities for organisations faced with the task of scheduling a mobile workforce. Given a mixed set of public and private transportation and a list of scheduling constraints, we seek to find solutions that are optimised with respect to the objectives of CO2 emissions and time. An optimiser, based on the NSGA-II algorithm, is used to find a range of solutions that offer the multiple options by trading CO2 emissions against time.

Solving a Real World Routing Problem Using Multiple Evolutionary Agents

This paper investigates the solving of a real world routing problem using evolutionary algorithms embedded within a Multi-agent system (MAS). An architecture for the MAS is proposed and mechanisms for controlling the interactions of agents are investigated. The control mechanism used in the final solution is based on the concept of agents submitting bids to receive work. The agents are also allowed to alter their bidding strategies as the solution improves. The MAS solves the test problem is solved, which previously could not be solved within the hard constraints.