Ant colony optimisation and local search for bin-packing and cutting stock problems

The Bin Packing Problem and the Cutting Stock Problem are two related classes of NP-hard combinatorial optimization problems. Exact solution methods can only be used for very small instances, so for real-world problems, we have to rely on heuristic methods. In recent years, researchers have started to apply evolutionary approaches to these problems, including Genetic Algorithms and Evolutionary Programming. In the work presented here, we used an ant colony optimization (ACO) approach to solve both Bin Packing and Cutting Stock Problems. We present a pure ACO approach, as well as an ACO approach augmented with a simple but very effective local search algorithm. It is shown that the pure ACO approach can compete with existing evolutionary methods, whereas the hybrid approach can outperform the best-known hybrid evolutionary solution methods for certain problem classes. The hybrid ACO approach is also shown to require different parameter values from the pure ACO approach and to give a more robust performance across different problems with a single set of parameter values. The local search algorithm is also run with random restarts and shown to perform significantly worse than when combined with ACO

A memetic algorithm based on Artificial Bee Colony for optimal synthesis of mechanisms

En este documento se presenta una propuesta novedosa de un algoritmo híbrido modular, como herramienta para resolver problemas de ingeniería del mundo real. Se implementa y aplica un algoritmo memético, MemMABC, para la solución de dos casos de diseño de mecanismos, con el fin de evaluar su eficiencia y rendimiento. El algoritmo propuesto es simple y flexible debido a su modularidad; estas características lo vuelven altamente reutilizable para ser aplicado en una amplia gama de problemas de optimización. Las soluciones de los casos de estudio también son modulares, siguiendo un esquema de programación estructurada que incluye el uso de variables globales para la configuración, y de subrutinas para la función objetivo y el manejo de las restricciones. Los algoritmos meméticos son una buena opción para resolver problemas duros de optimización, debido a la sinergia derivada de la combinación de sus componentes: una metaheurística poblacional para búsqueda global y un método de refinamiento local. La calidad en los resultados de las simulaciones sugiere que el MemMABC puede aplicarse con éxito para la solución de problemas duros de diseño en ingeniería.In this paper a novel proposal of a modular hybrid algorithm as a tool for solving real-world engineering problems is presented. A memetic algorithm, MemMABC, is implemented with this approach and applied to solve two case studies of mechanism design, in order to evaluate its efficiency and performance. Because of its modularity, the proposed algorithm is simple and flexible; these features make it quite reusable to be applied on different optimization problems, with a wide scope. The solutions of the optimization problems are also modular, following a scheme of structured programming that includes the use of global variables for configuration, and subroutines for the objective function and the restrictions. Memetic algorithms are a good option to solve hard optimization problems, because of the synergy derived from the combination of their components: a global search population-based metaheuristic and a local refinement method. The quality of simulation results suggests that MemMABC can be successfully applied to solve hard problems in engineering design.Peer Reviewe

Distributed constraint satisfaction for coordinating and integrating a large-scale, heterogeneous enterprise

Market forces are continuously driving public and private organisations towards higher productivity, shorter process and production times, and fewer labour hours. To cope with these changes, organisations are adopting new organisational models of coordination and cooperation that increase their flexibility, consistency, efficiency, productivity and profit margins. In this thesis an organisational model of coordination and cooperation is examined using a real life example; the technical integration of a distributed large-scale project of an international physics collaboration. The distributed resource constraint project scheduling problem is modelled and solved with the methods of distributed constraint satisfaction. A distributed local search method, the distributed breakout algorithm (DisBO), is used as the basis for the coordination scheme. The efficiency of the local search method is improved by extending it with an incremental problem solving scheme with variable ordering. The scheme is implemented as central algorithm, incremental breakout algorithm (IncBO), and as distributed algorithm, distributed incremental breakout algorithm (DisIncBO). In both cases, strong performance gains are observed for solving underconstrained problems. Distributed local search algorithms are incomplete and lack a termination guarantee. When problems contain hard or unsolvable subproblems and are tightly or overconstrained, local search falls into infinite cycles without explanation. A scheme is developed that identifies hard or unsolvable subproblems and orders these to size. This scheme is based on the constraint weight information generated by the breakout algorithm during search. This information, combined with the graph structure, is used to derive a fail first variable order. Empirical results show that the derived variable order is 'perfect'. When it guides simple backtracking, exceptionally hard problems do not occur, and, when problems are unsolvable, the fail depth is always the shortest. Two hybrid algorithms, BOBT and BOBT-SUSP are developed. When the problem is unsolvable, BOBT returns the minimal subproblem within the search scope and BOBT-SUSP returns the smallest unsolvable subproblem using a powerful weight sum constraint. A distributed hybrid algorithm (DisBOBT) is developed that combines DisBO with DisBT. The distributed hybrid algorithm first attempts to solve the problem with DisBO. If no solution is available after a bounded number of breakouts, DisBO is terminated, and DisBT solves the problem. DisBT is guided by a distributed variable order that is derived from the constraint weight information and the graph structure. The variable order is incrementally established, every time the partial solution needs to be extended, the next variable within the order is identified. Empirical results show strong performance gains, especially when problems are overconstrained and contain small unsolvable subproblems

Using micro genetic algorithm for solving scheduling problems

Job Shop Scheduling Problem (JSSP) and Timetable scheduling are known to be computationally NP–hard problems. There have been many attempts by many researchers to develop reliable scheduling software, however, many of these software have only been tested or applied on an experimental basis or on a small population with minimal constraints. However in actual model JSSP, the constraints involved are more complicated compared to classical JSSP and feasible schedule must be suggested within a short period of time. In this thesis, an enhanced micro GA, namely micro GA with local search is proposed to solve an actual model JSSP. The scheduler is able to generate an output of a set of feasible production plan not only at a faster rate but which can generate a plan which can reduce the makespan as compare to those using manual. Also, in this thesis, the micro GA is applied to the timetabling problem of Faculty of Electrical Engineering Universiti Teknologi Malaysia which has more than 3,000 students. Apart from having more students, the faculty also offers various different type s of specialized courses. Various constraints such as elective subjects, classrooms capacity, multiple sections students, lecturer, etc have to be taken into consideration when designing the solution for this problem. In this thesis , an enhanced micro GA is proposed for timetable scheduling in the Faculty to overcome the problems. The enhanced micro GA algorithm is referred to as distributed micro GA which has local search to speed up the scheduling process. Comparisons are made with simple GA methods such that a more optimal solution can be achieved. The proposed algorithm is successfully implemented at the Faculty meeting a variety of constraints not achievable using manual methods

Hiding Satisfying Assignments: Two are Better than One

The evaluation of incomplete satisfiability solvers depends critically on the availability of hard satisfiable instances. A plausible source of such instances consists of random k-SAT formulas whose clauses are chosen uniformly from among all clauses satisfying some randomly chosen truth assignment A. Unfortunately, instances generated in this manner tend to be relatively easy and can be solved efficiently by practical heuristics. Roughly speaking, as the formula's density increases, for a number of different algorithms, A acts as a stronger and stronger attractor. Motivated by recent results on the geometry of the space of satisfying truth assignments of random k-SAT and NAE-k-SAT formulas, we introduce a simple twist on this basic model, which appears to dramatically increase its hardness. Namely, in addition to forbidding the clauses violated by the hidden assignment A, we also forbid the clauses violated by its complement, so that both A and complement of A are satisfying. It appears that under this "symmetrization'' the effects of the two attractors largely cancel out, making it much harder for algorithms to find any truth assignment. We give theoretical and experimental evidence supporting this assertion.Comment: Preliminary version appeared in AAAI 200

A general framework of multi-population methods with clustering in undetectable dynamic environments

Copyright @ 2011 IEEETo solve dynamic optimization problems, multiple population methods are used to enhance the population diversity for an algorithm with the aim of maintaining multiple populations in different sub-areas in the fitness landscape. Many experimental studies have shown that locating and tracking multiple relatively good optima rather than a single global optimum is an effective idea in dynamic environments. However, several challenges need to be addressed when multi-population methods are applied, e.g., how to create multiple populations, how to maintain them in different sub-areas, and how to deal with the situation where changes can not be detected or predicted. To address these issues, this paper investigates a hierarchical clustering method to locate and track multiple optima for dynamic optimization problems. To deal with undetectable dynamic environments, this paper applies the random immigrants method without change detection based on a mechanism that can automatically reduce redundant individuals in the search space throughout the run. These methods are implemented into several research areas, including particle swarm optimization, genetic algorithm, and differential evolution. An experimental study is conducted based on the moving peaks benchmark to test the performance with several other algorithms from the literature. The experimental results show the efficiency of the clustering method for locating and tracking multiple optima in comparison with other algorithms based on multi-population methods on the moving peaks benchmark