Application of a new multi-agent Hybrid Co-evolution based Particle Swarm Optimisation methodology in ship design

In this paper, a multiple objective 'Hybrid Co-evolution based Particle Swarm Optimisation' methodology (HCPSO) is proposed. This methodology is able to handle multiple objective optimisation problems in the area of ship design, where the simultaneous optimisation of several conflicting objectives is considered. The proposed method is a hybrid technique that merges the features of co-evolution and Nash equilibrium with a ε-disturbance technique to eliminate the stagnation. The method also offers a way to identify an efficient set of Pareto (conflicting) designs and to select a preferred solution amongst these designs. The combination of co-evolution approach and Nash-optima contributes to HCPSO by utilising faster search and evolution characteristics. The design search is performed within a multi-agent design framework to facilitate distributed synchronous cooperation. The most widely used test functions from the formal literature of multiple objectives optimisation are utilised to test the HCPSO. In addition, a real case study, the internal subdivision problem of a ROPAX vessel, is provided to exemplify the applicability of the developed method

Uncertainty evaluation of reservoir simulation models using particle swarms and hierarchical clustering

History matching production data in finite difference reservoir simulation models has been and always will be a challenge for the industry. The principal hurdles that need to be overcome are finding a match in the first place and more importantly a set of matches that can capture the uncertainty range of the simulation model and to do this in as short a time as possible since the bottleneck in this process is the length of time taken to run the model. This study looks at the implementation of Particle Swarm Optimisation (PSO) in history matching finite difference simulation models. Particle Swarms are a class of evolutionary algorithms that have shown much promise over the last decade. This method draws parallels from the social interaction of swarms of bees, flocks of birds and shoals of fish. Essentially a swarm of agents are allowed to search the solution hyperspace keeping in memory each individual’s historical best position and iteratively improving the optimisation by the emergent interaction of the swarm. An intrinsic feature of PSO is its local search capability. A sequential niching variation of the PSO has been developed viz. Flexi-PSO that enhances the exploration and exploitation of the hyperspace and is capable of finding multiple minima. This new variation has been applied to history matching synthetic reservoir simulation models to find multiple distinct history 3 matches to try to capture the uncertainty range. Hierarchical clustering is then used to post-process the history match runs to reduce the size of the ensemble carried forward for prediction. The success of the uncertainty modelling exercise is then assessed by checking whether the production profile forecasts generated by the ensemble covers the truth case

Particle swarm optimization with composite particles in dynamic environments

This article is placed here with the permission of IEEE - Copyright @ 2010 IEEEIn recent years, there has been a growing interest in the study of particle swarm optimization (PSO) in dynamic environments. This paper presents a new PSO model, called PSO with composite particles (PSO-CP), to address dynamic optimization problems. PSO-CP partitions the swarm into a set of composite particles based on their similarity using a "worst first" principle. Inspired by the composite particle phenomenon in physics, the elementary members in each composite particle interact via a velocity-anisotropic reflection scheme to integrate valuable information for effectively and rapidly finding the promising optima in the search space. Each composite particle maintains the diversity by a scattering operator. In addition, an integral movement strategy is introduced to promote the swarm diversity. Experiments on a typical dynamic test benchmark problem provide a guideline for setting the involved parameters and show that PSO-CP is efficient in comparison with several state-of-the-art PSO algorithms for dynamic optimization problems.This work was supported in part by the Key Program of the National Natural Science Foundation (NNSF) of China under Grant 70931001 and 70771021, the Science Fund for Creative Research Group of the NNSF of China under Grant 60821063 and 70721001, the Ph.D. Programs Foundation of the Ministry of education of China under Grant 200801450008, and by the Engineering and Physical Sciences Research Council of U.K. under Grant EP/E060722/1

Dual level searching approach for solving multi-objective optimisation problems using hybrid particle swarm optimisation and bats echolocation-inspired algorithms

A dual level searching approach for multi objective optimisation problems using particle swarm optimisation and modified adaptive bats sonar algorithm is presented. The concept of echolocation of a colony of bats to find prey in the modified adaptive bats sonar algorithm is integrated with the established particle swarm optimisation algorithm. The proposed algorithm incorporates advantages of both particle swarm optimisation and modified adaptive bats sonar algorithm approach to handle the complexity of multi objective optimisation problems. These include swarm flight attitude and swarm searching strategy. The performance of the algorithm is verified through several multi objective optimisation benchmark test functions and problem. The acquired results show that the proposed algorithm perform well to produce a reliable Pareto front. The proposed algorithm can thus be an effective method for solving of multi objective optimisation problems

Evolutionary population dynamics and multi-objective optimisation problems

Griffith Sciences, School of Information and Communication TechnologyFull Tex

Nature-inspired algorithms for solving some hard numerical problems

Optimisation is a branch of mathematics that was developed to find the optimal solutions, among all the possible ones, for a given problem. Applications of optimisation techniques are currently employed in engineering, computing, and industrial problems. Therefore, optimisation is a very active research area, leading to the publication of a large number of methods to solve specific problems to its optimality. This dissertation focuses on the adaptation of two nature inspired algorithms that, based on optimisation techniques, are able to compute approximations for zeros of polynomials and roots of non-linear equations and systems of non-linear equations. Although many iterative methods for finding all the roots of a given function already exist, they usually require: (a) repeated deflations, that can lead to very inaccurate results due to the problem of accumulating rounding errors, (b) good initial approximations to the roots for the algorithm converge, or (c) the computation of first or second order derivatives, which besides being computationally intensive, it is not always possible. The drawbacks previously mentioned served as motivation for the use of Particle Swarm Optimisation (PSO) and Artificial Neural Networks (ANNs) for root-finding, since they are known, respectively, for their ability to explore high-dimensional spaces (not requiring good initial approximations) and for their capability to model complex problems. Besides that, both methods do not need repeated deflations, nor derivative information. The algorithms were described throughout this document and tested using a test suite of hard numerical problems in science and engineering. Results, in turn, were compared with several results available on the literature and with the well-known Durand–Kerner method, depicting that both algorithms are effective to solve the numerical problems considered.A Optimização é um ramo da matemática desenvolvido para encontrar as soluções óptimas, de entre todas as possíveis, para um determinado problema. Actualmente, são várias as técnicas de optimização aplicadas a problemas de engenharia, de informática e da indústria. Dada a grande panóplia de aplicações, existem inúmeros trabalhos publicados que propõem métodos para resolver, de forma óptima, problemas específicos. Esta dissertação foca-se na adaptação de dois algoritmos inspirados na natureza que, tendo como base técnicas de optimização, são capazes de calcular aproximações para zeros de polinómios e raízes de equações não lineares e sistemas de equações não lineares. Embora já existam muitos métodos iterativos para encontrar todas as raízes ou zeros de uma função, eles usualmente exigem: (a) deflações repetidas, que podem levar a resultados muito inexactos, devido ao problema da acumulação de erros de arredondamento a cada iteração; (b) boas aproximações iniciais para as raízes para o algoritmo convergir, ou (c) o cálculo de derivadas de primeira ou de segunda ordem que, além de ser computacionalmente intensivo, para muitas funções é impossível de se calcular. Estas desvantagens motivaram o uso da Optimização por Enxame de Partículas (PSO) e de Redes Neurais Artificiais (RNAs) para o cálculo de raízes. Estas técnicas são conhecidas, respectivamente, pela sua capacidade de explorar espaços de dimensão superior (não exigindo boas aproximações iniciais) e pela sua capacidade de modelar problemas complexos. Além disto, tais técnicas não necessitam de deflações repetidas, nem do cálculo de derivadas. Ao longo deste documento, os algoritmos são descritos e testados, usando um conjunto de problemas numéricos com aplicações nas ciências e na engenharia. Os resultados foram comparados com outros disponíveis na literatura e com o método de Durand–Kerner, e sugerem que ambos os algoritmos são capazes de resolver os problemas numéricos considerados