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

Observation of Nonspreading Wave Packets in an Imaginary Potential

We propose and experimentally demonstrate a method to prepare a nonspreading atomic wave packet. Our technique relies on a spatially modulated absorption constantly chiseling away from an initially broad de Broglie wave. The resulting contraction is balanced by dispersion due to Heisenberg's uncertainty principle. This quantum evolution results in the formation of a nonspreading wave packet of Gaussian form with a spatially quadratic phase. Experimentally, we confirm these predictions by observing the evolution of the momentum distribution. Moreover, by employing interferometric techniques, we measure the predicted quadratic phase across the wave packet. Nonspreading wave packets of this kind also exist in two space dimensions and we can control their amplitude and phase using optical elements.Comment: 4 figure

SITAR—a useful instrument for growth curve analysis

Background Growth curve analysis is a statistical issue in life course epidemiology. Height in puberty involves a growth spurt, the timing and intensity of which varies between individuals. Such data can be summarized with individual Preece–Baines (PB) curves, and their five parameters then related to earlier exposures or later outcomes. But it involves fitting many curves

Ownership and control in a competitive industry

We study a differentiated product market in which an investor initially owns a controlling stake in one of two competing firms and may acquire a non-controlling or a controlling stake in a competitor, either directly using her own assets, or indirectly via the controlled firm. While industry profits are maximized within a symmetric two product monopoly, the investor attains this only in exceptional cases. Instead, she sometimes acquires a noncontrolling stake. Or she invests asymmetrically rather than pursuing a full takeover if she acquires a controlling one. Generally, she invests indirectly if she only wants to affect the product market outcome, and directly if acquiring shares is profitable per se. --differentiated products,separation of ownership and control,private benefits of control

Ant colony optimization for power plant maintenance scheduling optimization - a five-station hydropower system

The original publication can be found at www.springerlink.comA number of algorithms have been developed for the optimization of power plant maintenance schedules. However, the true test of such algorithms occurs when they are applied to real systems. In this paper, the application of an Ant Colony Optimization formulation to a hydropower system is presented. The formulation is found to be effective in handling various constraints commonly encountered in practice. Overall, the results obtained using the ACO formulation are better than those given by traditional methods using engineering judgment, which indicates the potential of ACO in solving realistic power plant maintenance scheduling problems.Wai Kuan Foong, Angus R. Simpson, Holger R. Maier and Stephen Stol

Application of two ant colony optimisation algorithms to water distribution system optimisation

Water distribution systems (WDSs) are costly infrastructure in terms of materials, construction, maintenance, and energy requirements. Much attention has been given to the application of optimisation methods to minimise the costs associated with such infrastructure. Historically, traditional optimisation techniques have been used, such as linear and non-linear programming, but within the past decade the focus has shifted to the use of heuristics derived from nature (HDNs), for example Genetic Algorithms, Simulated Annealing and more recently Ant Colony Optimisation (ACO). ACO, as an optimisation process, is based on the analogy of the foraging behaviour of a colony of searching ants, and their ability to determine the shortest route between their nest and a food source. Many different formulations of ACO algorithms exist that are aimed at providing advancements on the original and most basic formulation, Ant System (AS). These advancements differ in their utilisation of information learned about a search-space to manage two conflicting aspects of an algorithm's searching behaviour. These aspects are termed 'exploration' and 'exploitation'. Exploration is an algorithm's ability to search broadly through the problem's search space and exploitation is an algorithm's ability to search locally around good solutions that have been found previously. One such advanced ACO algorithm, which is implemented within this paper, is the Max-Min Ant System (MMAS). This algorithm encourages local searching around the best solution found in each iteration, while implementing methods that slow convergence and facilitate exploration. In this paper, the performance of MMAS is compared to that of AS for two commonly used WDS case studies, the New York Tunnels Problem and the Hanoi Problem. The sophistication of MMAS is shown to be effective as it outperforms AS and performs better than any other HDN in the literature for both case studies considered. © 2005 Elsevier Ltd. All rights reserved.http://www.elsevier.com/wps/find/journaldescription.cws_home/623/description#descriptio

Beyond ‘geo-economics’: advanced unevenness and the anatomy of German austerity

This article aims to shed new light on Germany’s domineering role in the eurocrisis. I argue that the realist-inspired depiction of Germany as a ‘geo-economic power’, locked into zero-sum competition with its European partners, is built around an empty core: unable to theorise how anarchy shapes the calculus of states where security competition has receded, it cannot explain why German state managers have insisted on an austerity response to the crisis despite its significant risks and costs even for Germany itself. To unlock this puzzle, this article outlines a version of uneven and combined development (UCD) that is better able to capture the international pressures and opportunities faced by policy elites in advanced capitalist states that no longer encounter one another as direct security rivals. Applied to Germany, this lens reveals a twofold unevenness in the historical structures and growth cycles of capitalist economies that shape its contradictory choice for austerity. In the long run, the reorientation of the export-dependent German economy from Europe towards Asian and Latin American late industrialisers renders the structural adjustment of the eurozone an opportunity—from the cost-saving view of German manufacturers producing in the European home market for export abroad, as well as for German state officials keen to sustain a crumbling class compromise centred on Germany’s world market success. In the short term, however, its exposed position between the divergent post-crisis trajectories of the US and Europe accelerates pressures for austerity beyond what German state and corporate elites would otherwise consider feasible

On the choice of the update strength in estimation-of-distribution algorithms and ant colony optimization

Probabilistic model-building Genetic Algorithms (PMBGAs) are a class of metaheuristics that evolve probability distributions favoring optimal solutions in the underlying search space by repeatedly sampling from the distribution and updating it according to promising samples. We provide a rigorous runtime analysis concerning the update strength, a vital parameter in PMBGAs such as the step size 1 / K in the so-called compact Genetic Algorithm (cGA) and the evaporation factor ρ in ant colony optimizers (ACO). While a large update strength is desirable for exploitation, there is a general trade-off: too strong updates can lead to unstable behavior and possibly poor performance. We demonstrate this trade-off for the cGA and a simple ACO algorithm on the well-known OneMax function. More precisely, we obtain lower bounds on the expected runtime of Ω(Kn−−√+nlogn) and Ω(n−−√/ρ+nlogn), respectively, suggesting that the update strength should be limited to 1/K,ρ=O(1/(n−−√logn)). In fact, choosing 1/K,ρ∼1/(n−−√logn) both algorithms efficiently optimize OneMax in expected time Θ(nlogn). Our analyses provide new insights into the stochastic behavior of PMBGAs and propose new guidelines for setting the update strength in global optimization

An Empirical Study of Off-line Configuration and On-line Adaptation in Operator Selection

Automating the process of finding good parameter settings is important in the design of high-performing algorithms. These automatic processes can generally be categorized into off-line and on-line methods. Off-line configuration consists in learning and selecting the best setting in a training phase, and usually fixes it while solving an instance. On-line adaptation methods on the contrary vary the parameter setting adaptively during each algorithm run. In this work, we provide an empirical study of both approaches on the operator selection problem, explore the possibility of varying parameter value by a non-adaptive distribution tuned off-line, and incorporate the off-line with on-line approaches. In particular, using an off-line tuned distribution to vary parameter values at runtime appears to be a promising idea for automatic configuration. © 2014 Springer International Publishing.SCOPUS: cp.kinfo:eu-repo/semantics/publishe