Injecting problem-dependent knowledge to improve evolutionary optimization search ability

The flexibility introduced by evolutionary algorithms (EAs) has allowed the use of virtually arbitrary objective functions and constraints even when evaluations require, as for real-world problems, running complex mathematical and/or procedural simulations of the systems under analysis. Even so, EAs are not a panacea. Traditionally, the solution search process has been totally oblivious of the specific problem being solved, and optimization processes have been applied regardless of the size, complexity, and domain of the problem. In this paper, we justify our claim that far-reaching benefits may be obtained from more directly influencing how searches are performed. We propose using data mining techniques as a step for dynamically generating knowledge that can be used to improve the efficiency of solution search processes. In this paper, we use Kohonen SOMs and show an application for a well-known benchmark problem in the water distribution system design literature. The result crystallizes the conceptual rules for the EA to apply at certain stages of the evolution, which reduces the search space and accelerates convergence. (C) 2015 Elsevier B.V. All rights reserved.Izquierdo Sebastián, J.; Campbell-Gonzalez, E.; Montalvo Arango, I.; Pérez García, R. (2016). Injecting problem-dependent knowledge to improve evolutionary optimization search ability. Journal of Computational and Applied Mathematics. 291:281-292. doi:10.1016/j.cam.2015.03.019S28129229

Methodology for identifying alternative solutions in a population based data generation approach applied to synthetic biology

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonDesign is an essential component of sustainable development. Computational modelling has become a useful technique that facilitates the design of complex systems. Variables that characterises a complex system are encoded into a computational model using mathematical concepts and through simulation each of these variables alone or in combination are modified to observe the changes in the outcome. This allows the researchers to make predictions on the behaviour of the real system that is being studied in response to the changes. The ultimate goal of any design process is to come up with the best design; as resources are limited, to minimize the cost and resource consumption, and to maximize the performance, profits and efficiency. To optimize means to find the best solution, the best compromise among several conflicting demands subject to predefined requirements. Therefore, computational optimization, modelling and simulation forms an integrated part of the modern design practice. This thesis defines a data analytics driven methodology which enables the identification of alternative solutions of computational design by analysing the generational history of the population based heuristic search used to generate the templates. While optimisation is focused on obtaining the optimal solution this methodology focuses on alternative solutions which are sub optimal by fitness or solutions with similar fitness but different structures. When the optimal design solution is less robust, alternative solutions can offer a sufficiently good accuracy and an achievable resource requirement. The main advantage of the methodology is that it exploits the exploration process of the solution space during a single run, by focusing also on suboptimal solutions, which usually get neglected in the search for an optimal one. The history of the heuristic search is analysed for the emergence of alternative solutions and evolving of a solution. By examining how an initial solution converts to an optimal solution core design patterns are identified, and these were used to improve the design process. Further, this method limits the number of runs of the heuristic search as more solution space is covered. The methodology is generic because it can be used to any instance where a population based heuristic search is applied to generate optimal designs. The applicability of the methodology is demonstrated using three case studies from mathematics (building of a mathematical function for a set target) and biology (obtaining alternative designs for genomic metabolic models [GEM] and DNA walker circuits). In each case a different heuristic search method was used: Gene expression programming (mathematical expressions), genetic algorithms (GEM models) and simulated annealing (DNA walker circuits). Descriptive analytics, visual analytics and clustering was mainly used to build the data analytics driven approach in identifying alternative solutions. This data analytics driven methodology is useful in optimising the computational design of complex systems

Optimisation par essaim particulaire (adaptation de tribes à l'optimisation multiobjectif)

Dans le cadre de l'optimisation multiobjectif, les métaheuristiques sont reconnues pour être des méthodes performantes mais elles ne rencontrent qu'un succès modéré dans le monde de l'industrie. Dans un milieu où seule la performance compte, l'aspect stochastique des métaheuristiques semble encore être un obstacle difficile à franchir pour les décisionnaires. Il est donc important que les chercheurs de la communauté portent un effort tout particulier sur la facilité de prise en main des algorithmes. Plus les algorithmes seront faciles d'accès pour les utilisateurs novices, plus l'utilisation de ceux-ci pourra se répandre. Parmi les améliorations possibles, la réduction du nombre de paramètres des algorithmes apparaît comme un enjeu majeur. En effet, les métaheuristiques sont fortement dépendantes de leur jeu de paramètres. Dans ce cadre se situe l'apport majeur de TRIBES, un algorithme mono-objectif d'Optimisation par Essaim Particulaire (OEP) qui fonctionne automatiquement,sans paramètres. Il a été mis au point par Maurice Clerc. En fait, le fonctionnement de l'OEP nécessite la manipulation de plusieurs paramètres. De ce fait, TRIBES évite l'effort de les régler (taille de l'essaim, vitesse maximale, facteur d'inertie, etc.).Nous proposons dans cette thèse une adaptation de TRIBES à l'optimisation multiobjectif. L'objectif est d'obtenir un algorithme d'optimisation par essaim particulaire multiobjectif sans paramètres de contrôle. Nous reprenons les principaux mécanismes de TRIBES auxquels sont ajoutés de nouveaux mécanismes destinés à traiter des problèmes multiobjectif. Après les expérimentations, nous avons constaté, que TRIBES-Multiobjectif est moins compétitif par rapport aux algorithmes de référence dans la littérature. Ceci peut être expliqué par la stagnation prématurée de l'essaim. Pour remédier à ces problèmes, nous avons proposé l'hybridation entre TRIBES-Multiobjectif et un algorithme de recherche locale, à savoir le recuit simulé et la recherche tabou. L'idée était d'améliorer la capacité d'exploitation deTRIBES-Multiobjectif. Nos algorithmes ont été finalement appliqués sur des problèmes de dimensionnement des transistors dans les circuits analogiquesMeta-heuristics are recognized to be successful to deal with multiobjective optimization problems but still with limited success in engineering fields. In an environment where only the performance counts, the stochastic aspect of meta-heuristics again seems to be a difficult obstacle to cross for the decision-makers. It is, thus, important that the researchers of the community concern a quite particular effort to ease the handling of those algorithms. The more the algorithms will be easily accessible for the novices, the more the use of these algorithms can spread. Among the possible improvements, reducing the number of parameters is considered as the most challenging one. In fact, the performance of meta-heuristics is strongly dependent on their parameters values. TRIBES presents an attempt to remedy this problem. In fact, it is a particle swarm optimization (PSO) algorithm that works in an autonomous way. It was proposed by Maurice Clerc. Indeed, like every other meta-heuristic, PSO requires many parameters to be fitted every time a new problem is considered. The major contribution of TRIBES is to avoid the effort of fitting them. We propose, in this thesis, an adaptation of TRIBES to the multiobjective optimization. Our aim is to conceive a competitive PSO algorithm free of parameters. We consider the main mechanisms of TRIBES to which are added new mechanisms intended to handle multiobjective problems. After the experimentations, we noticed that Multiobjective-TRIBESis not competitive compared to other multiobjective algorithms representative of the state of art. It can be explained by the premature stagnation of the swarm. To remedy these problems, we proposed the hybridization between Multiobjective-TRIBES and local search algorithms such as simulated annealing and tabu search. The idea behind the hybridization was to improve the capacity of exploitation of Multiobjective-TRIBES. Our algorithms were finally applied to sizing analogical circuits' problemsPARIS-EST-Université (770839901) / SudocSudocFranceF

An Algorithm for Evolving Protocol Constraints

Centre for Intelligent Systems and their ApplicationsWe present an investigation into the design of an evolutionary mechanism for multiagent protocol constraint optimisation. Starting with a review of common population based mechanisms we discuss the properties of the mechanisms used by these search methods. We derive a novel algorithm for optimisation of vectors of real numbers and empirically validate the efficacy of the design by comparing against well known results from the literature. We discuss the application of an optimiser to a novel problem and remark upon the relevance of the no free lunch theorem. We show the relative performance of the optimiser is strong and publish details of a new best result for the Keane optimisation problem. We apply the final algorithm to the multi-agent protocol optimisation problem and show the design process was successful

Annual Report 2018-2019

It contains the statement of R&D works undertaken, achievement made and the expenditure by the laboratory during the financial year 2018-2019

Simple and Adaptive Particle Swarms

The substantial advances that have been made to both the theoretical and practical aspects of particle swarm optimization over the past 10 years have taken it far beyond its original intent as a biological swarm simulation. This thesis details and explains these advances in the context of what has been achieved to this point, as well as what has yet to be understood or solidified within the research community. Taking into account the state of the modern field, a standardized PSO algorithm is defined for benchmarking and comparative purposes both within the work, and for the community as a whole. This standard is refined and simplified over several iterations into a form that does away with potentially undesirable properties of the standard algorithm while retaining equivalent or superior performance on the common set of benchmarks. This refinement, referred to as a discrete recombinant swarm (PSODRS) requires only a single user-defined parameter in the positional update equation, and uses minimal additive stochasticity, rather than the multiplicative stochasticity inherent in the standard PSO. After a mathematical analysis of the PSO-DRS algorithm, an adaptive framework is developed and rigorously tested, demonstrating the effects of the tunable particle- and swarm-level parameters. This adaptability shows practical benefit by broadening the range of problems which the PSO-DRS algorithm is wellsuited to optimize

A Framework for Hyper-Heuristic Optimisation of Conceptual Aircraft Structural Designs

Conceptual aircraft structural design concerns the generation of an airframe that will provide sufficient strength under the loads encountered during the operation of the aircraft. In providing such strength, the airframe greatly contributes to the mass of the vehicle, where an excessively heavy design can penalise the performance and cost of the aircraft. Structural mass optimisation aims to minimise the airframe weight whilst maintaining adequate resistance to load. The traditional approach to such optimisation applies a single optimisation technique within a static process, which prevents adaptation of the optimisation process to react to changes in the problem. Hyper-heuristic optimisation is an evolving field of research wherein the optimisation process is evaluated and modified in an attempt to improve its performance, and thus the quality of solutions generated. Due to its relative infancy, hyper-heuristics have not been applied to the problem of aircraft structural design optimisation. It is the thesis of this research that hyper-heuristics can be employed within a framework to improve the quality of airframe designs generated without incurring additional computational cost. A framework has been developed to perform hyper-heuristic structural optimisation of a conceptual aircraft design. Four aspects of hyper-heuristics are included within the framework to promote improved process performance and subsequent solution quality. These aspects select multiple optimisation techniques to apply to the problem, analyse the solution space neighbouring good designs and adapt the process based on its performance. The framework has been evaluated through its implementation as a purpose-built computational tool called AStrO. The results of this evaluation have shown that significantly lighter airframe designs can be generated using hyper-heuristics than are obtainable by traditional optimisation approaches. Moreover, this is possible without penalising airframe strength or necessarily increasing computational costs. Furthermore, improvements are possible over the existing aircraft designs currently in production and operation

Application of PSO for optimization of power systems under uncertainty

The primary objective of this dissertation is to develop a black box optimization tool. The algorithm should be able to solve complex nonlinear, multimodal, discontinuous and mixed-integer power system optimization problems without any model reduction. Although there are many computational intelligence (CI) based algorithms which can handle these problems, they require intense human intervention in the form of parameter tuning, selection of a suitable algorithm for a given problem etc. The idea here is to develop an algorithm that works relatively well on a variety of problems with minimum human effort. An adaptive particle swarm optimization algorithm (PSO) is presented in this thesis. The algorithm has special features like adaptive swarm size, parameter free update strategies, progressive neighbourhood topologies, self learning parameter free penalty approach etc. The most significant optimization task in the power system operation is the scheduling of various generation resources (Unit Commitment, UC). The current practice used in UC modelling is the binary approach. This modelling results in a high dimension problem. This in turn leads to increased computational effort and decreased efficiency of the algorithm. A duty cycle based modelling proposed in this thesis results in 80 percent reduction in the problem dimension. The stern uptime and downtime requirements are also included in the modelling. Therefore, the search process mostly starts in a feasible solution space. From the investigations on a benchmark problem, it was found that the new modelling results in high quality solutions along with improved convergence. The final focus of this thesis is to investigate the impact of unpredictable nature of demand and renewable generation on the power system operation. These quantities should be treated as a stochastic processes evolving over time. A new PSO based uncertainty modelling technique is used to abolish the restrictions imposed by the conventional modelling algorithms. The stochastic models are able to incorporate the information regarding the uncertainties and generate day ahead UC schedule that are optimal to not just the forecasted scenario for the demand and renewable generation in feed but also to all possible set of scenarios. These models will assist the operator to plan the operation of the power system considering the stochastic nature of the uncertainties. The power system can therefore optimally handle huge penetration of renewable generation to provide economic operation maintaining the same reliability as it was before the introduction of uncertainty