Geometric Particle Swarm Optimization for Multi-objective Optimization Using Decomposition

Multi-objective evolutionary algorithms (MOEAs) based on decomposition are aggregation-based algorithms which transform a multi-objective optimization problem (MOP) into several single-objective subproblems. Being effective, efficient, and easy to implement, Particle Swarm Optimization (PSO) has become one of the most popular single-objective optimizers for continuous problems, and recently it has been successfully extended to the multi-objective domain. However, no investigation on the application of PSO within a multi-objective decomposition framework exists in the context of combinatorial optimization. This is precisely the focus of the paper. More specifically, we study the incorporation of Geometric Particle Swarm Optimization (GPSO), a discrete generalization of PSO that has proven successful on a number of single-objective combinatorial problems, into a decomposition approach. We conduct experiments on manyobjective 1/0 knapsack problems i.e. problems with more than three objectives functions, substantially harder than multi-objective problems with fewer objectives. The results indicate that the proposed multi-objective GPSO based on decomposition is able to outperform two version of the wellknow MOEA based on decomposition (MOEA/D) and the most recent version of the non-dominated sorting genetic algorithm (NSGA-III), which are state-of-the-art multi-objective evolutionary approaches based on decomposition

Improving the Bin Packing Heuristic through Grammatical Evolution Based on Swarm Intelligence

In recent years Grammatical Evolution (GE) has been used as a representation of Genetic Programming (GP) which has been applied to many optimization problems such as symbolic regression, classification, Boolean functions, constructed problems, and algorithmic problems. GE can use a diversity of searching strategies including Swarm Intelligence (SI). Particle Swarm Optimisation (PSO) is an algorithm of SI that has two main problems: premature convergence and poor diversity. Particle Evolutionary Swarm Optimization (PESO) is a recent and novel algorithm which is also part of SI. PESO uses two perturbations to avoid PSO’s problems. In this paper we propose using PESO and PSO in the frame of GE as strategies to generate heuristics that solve the Bin Packing Problem (BPP); it is possible however to apply this methodology to other kinds of problems using another Grammar designed for that problem. A comparison between PESO, PSO, and BPP’s heuristics is performed through the nonparametric Friedman test. The main contribution of this paper is proposing a Grammar to generate online and offline heuristics depending on the test instance trying to improve the heuristics generated by other grammars and humans; it also proposes a way to implement different algorithms as search strategies in GE like PESO to obtain better results than those obtained by PSO

Enhancing numerical modelling efficiency for electromagnetic simulation of physical layer components.

The purpose of this thesis is to present solutions to overcome several key difficulties that limit the application of numerical modelling in communication cable design and analysis. In particular, specific limiting factors are that simulations are time consuming, and the process of comparison requires skill and is poorly defined and understood. When much of the process of design consists of optimisation of performance within a well defined domain, the use of artificial intelligence techniques may reduce or remove the need for human interaction in the design process. The automation of human processes allows round-the-clock operation at a faster throughput. Achieving a speedup would permit greater exploration of the possible designs, improving understanding of the domain. This thesis presents work that relates to three facets of the efficiency of numerical modelling: minimizing simulation execution time, controlling optimization processes and quantifying comparisons of results. These topics are of interest because simulation times for most problems of interest run into tens of hours. The design process for most systems being modelled may be considered an optimisation process in so far as the design is improved based upon a comparison of the test results with a specification. Development of software to automate this process permits the improvements to continue outside working hours, and produces decisions unaffected by the psychological state of a human operator. Improved performance of simulation tools would facilitate exploration of more variations on a design, which would improve understanding of the problem domain, promoting a virtuous circle of design. The minimization of execution time was achieved through the development of a Parallel TLM Solver which did not use specialized hardware or a dedicated network. Its design was novel because it was intended to operate on a network of heterogeneous machines in a manner which was fault tolerant, and included a means to reduce vulnerability of simulated data without encryption. Optimisation processes were controlled by genetic algorithms and particle swarm optimisation which were novel applications in communication cable design. The work extended the range of cable parameters, reducing conductor diameters for twisted pair cables, and reducing optical coverage of screens for a given shielding effectiveness. Work on the comparison of results introduced ―Colour maps‖ as a way of displaying three scalar variables over a two-dimensional surface, and comparisons were quantified by extending 1D Feature Selective Validation (FSV) to two dimensions, using an ellipse shaped filter, in such a way that it could be extended to higher dimensions. In so doing, some problems with FSV were detected, and suggestions for overcoming these presented: such as the special case of zero valued DC signals. A re-description of Feature Selective Validation, using Jacobians and tensors is proposed, in order to facilitate its implementation in higher dimensional spaces

Using Particle Swarm Optimization for Market Timing Strategies

Market timing is the issue of deciding when to buy or sell a given asset on the market. As one of the core issues of algorithmic trading systems, designers of such system have turned to computational intelligence methods to aid them in this task. In this thesis, we explore the use of Particle Swarm Optimization (PSO) within the domain of market timing.nPSO is a search metaheuristic that was first introduced in 1995 [28] and is based on the behavior of birds in flight. Since its inception, the PSO metaheuristic has seen extensions to adapt it to a variety of problems including single objective optimization, multiobjective optimization, niching and dynamic optimization problems. Although popular in other domains, PSO has seen limited application to the issue of market timing. The current incumbent algorithm within the market timing domain is Genetic Algorithms (GA), based on the volume of publications as noted in [40] and [84]. In this thesis, we use PSO to compose market timing strategies using technical analysis indicators. Our first contribution is to use a formulation that considers both the selection of components and the tuning of their parameters in a simultaneous manner, and approach market timing as a single objective optimization problem. Current approaches only considers one of those aspects at a time: either selecting from a set of components with fixed values for their parameters or tuning the parameters of a preset selection of components. Our second contribution is proposing a novel training and testing methodology that explicitly exposes candidate market timing strategies to numerous price trends to reduce the likelihood of overfitting to a particular trend and give a better approximation of performance under various market conditions. Our final contribution is to consider market timing as a multiobjective optimization problem, optimizing five financial metrics and comparing the performance of our PSO variants against a well established multiobjective optimization algorithm. These algorithms address unexplored research areas in the context of PSO algorithms to the best of our knowledge, and are therefore original contributions. The computational results over a range of datasets shows that the proposed PSO algorithms are competitive to GAs using the same formulation. Additionally, the multiobjective variant of our PSO algorithm achieve statistically significant improvements over NSGA-II