Hybrid nature-inspired computation methods for optimization

The focus of this work is on the exploration of the hybrid Nature-Inspired Computation (NIC) methods with application in optimization. In the dissertation, we first study various types of the NIC algorithms including the Clonal Selection Algorithm (CSA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Simulated Annealing (SA), Harmony Search (HS), Differential Evolution (DE), and Mind Evolution Computing (MEC), and propose several new fusions of the NIC techniques, such as CSA-DE, HS-DE, and CSA-SA. Their working principles, structures, and algorithms are analyzed and discussed in details. We next investigate the performances of our hybrid NIC methods in handling nonlinear, multi-modal, and dynamical optimization problems, e.g., nonlinear function optimization, optimal LC passive power filter design, and optimization of neural networks and fuzzy classification systems. The hybridization of these NIC methods can overcome the shortcomings of standalone algorithms while still retaining all the advantages. It has been demonstrated using computer simulations that the proposed hybrid NIC approaches are capable of yielding superior optimization performances over the individual NIC methods as well as conventional methodologies with regard to the search efficiency, convergence speed, and quantity and quality of the optimal solutions achieved

Improving the bees algorithm for complex optimisation problems

An improved swarm-based optimisation algorithm from the Bees Algorithm family for solving complex optimisation problems is proposed. Like other Bees Algorithms, the algorithm performs a form of exploitative local search combined with random exploratory global search. This thesis details the development and optimisation of this algorithm and demonstrates its robustness. The development includes a new method of tuning the Bees Algorithm called Meta Bees Algorithm and the functionality of the proposed method is compared to the standard Bees Algorithm and to a range of state-of-the-art optimisation algorithms. A new fitness evaluation method has been developed to enable the Bees Algorithm to solve a stochastic optimisation problem. The new modified Bees Algorithm was tested on the optimisation of parameter values for the Ant Colony Optimisation algorithm when solving Travelling Salesman Problems. Finally, the Bees Algorithm has been adapted and employed to solve complex combinatorial problems. The algorithm has been combined with two neighbourhood operators to solve such problems. The performance of the proposed Bees Algorithm has been tested on a number of travelling salesman problems, including two problems on printed circuit board assembly machine sequencing

Algorithmes pour la prise de décision distribuée en contexte hiérarchique

RÉSUMÉ Cette thèse a pour objet la coordination entre entités autonomes. De manière plus précise, nous nous intéressons à la coordination dans un contexte hiérarchique. Les problèmes étudiés montrent les caractéristiques suivantes : (1) il s’agit de problèmes d’optimisation distribués, (2) le problème est naturellement décomposé en sousproblèmes, (3) il existe a priori une séquence selon laquelle les sous-problèmes doivent être résolus, (4) les sous-problèmes sont sous la responsabilité de différentes entités et (5) chaque sous-problème est défini en fonction des solutions retenues pour les sousproblèmes précédents. Parmi les principaux domaines d’application, on trouve les systèmes d’aide à la décision organisationnels et les problèmes de synchronisation dans les chaînes logistiques industrielles. Ce dernier domaine sert de fil conducteur dans cette thèse : le travail de plusieurs unités de production est nécessaire pour fabriquer et livrer les commandes des clients. Différentes alternatives sont possibles en ce qui a trait aux pièces à utiliser, au choix des processus de fabrication, à l’ordonnancement des opérations et au transport. Chaque partenaire désire établir son plan de production (quoi faire, où et quand le faire), mais il est nécessaire pour eux de coordonner leurs activités. Les méthodes utilisées en pratique industrielle peuvent être qualifiées d’heuristiques de coordination. À l’opposé, il existe des algorithmes d’optimisation distribués et exacts, notamment les techniques de raisonnement sur contraintes distribuées (Distributed Constraint Optimization Problems, ou DCOP). Cependant, ces derniers algorithmes s’accommodent mal de la nature hiérarchique des problèmes étudiés et pourraient difficilement être utilisés en pratique. Les forces et les faiblesses des méthodes heuristiques et exactes nous ont donc amené à proposer de nouvelles approches.---------- ABSTRACT This thesis concerns multiagent coordination in hierarchical settings. These are distributed optimization problems showing the following characteristics: (1) the global problem is naturally decomposed into subproblems, (2) a sequence, defined a priori, exists in which the subproblems must be solved, (3) various agents are responsible for the subproblems, and (4) each subproblem is defined according to the solutions adopted for the preceding subproblems. Organizational distributed decision making and Supply chain coordination are among the main application domains. The latter case is more thoroughly studied in this thesis. In this kind of problem, the cooperation of several facilities is needed to produce and deliver the products ordered by external customers. However, different alternatives are possible regarding the parts to use, the manufacturing processes to follow, the scheduling of operations and the choice of transportation. Therefore, supply chain partners must coordinate their local decisions (e.g. what to do, where and when), with the common objective of delivering the ordered products with the least possible delay. The most commonly used coordination mechanisms can be described as heuristics. In contrast, some generic and complete distributed algorithms exist – researchers in Distributed Artificial Intelligence (DAI) have proposed a generic framework called Distributed Constraint Optimization Problem (DCOP). However, there are certain difficulties in mapping the actual business context (which is highly hierarchical) into the DCOP framework. Thus, based on the strengths and weaknesses of both the complete and heuristic approaches, we propose new approaches

Boosting ACO with a Preprocessing Step

When solving a combinatorial optimization problem with the Ant Colony Optimization (ACO) metaheuristic, one usually has to nd a compromise between guiding or diversifying the search. Indeed, ACO uses pheromone to attract ants. When increasing the sensibility of ants to pheromone, they converge quicker towards a solution but, as a counterpart, they usually nd worse solutions. In this paper, we rst study the inuence of ACO parameters on the exploratory ability of ants. We then study the evolution of the impact of pheromone during the solution process with respect to its cost's management. We nally propose to introduce a preprocessing step that actually favors a larger exploration of the search space at the beginning of the search at low cost. We illustrate our approach on Ant-Solver, an ACO algorithm that has been designed to solve Constraint Satisfaction Problems, and we show on random binary problems that it allows to nd better solutions more than twice quicker