Ant colony optimization applied to water distribution system design: Comparative study of five algorithms

Water distribution systems WDSs are costly infrastructure, and much attention has been given to the application of optimization methods to minimize design costs. In previous studies, ant colony optimization ACO has been found to perform extremely competitively for WDS optimization. In this paper, five ACO algorithms are tested: one basic algorithm ant system and four more advanced algorithms ant colony system, elitist ant system, elitist-rank ant system ASrank , and max-min ant system MMAS . Experiments are carried out to determine their performance on four WDS case studies, three of which have been considered widely in the literature. The findings of the study show that some ACO algorithms are very successful for WDS design, as two of the ACO algorithms MMAS and ASrank outperform all other algorithms applied to these case studies in the literature.Aaron C. Zecchin, Holger R. Maier, Angus R. Simpson, Michael Leonard, and John B. Nixo

Ant colony system for a VRP with multiple time windows and multiple visits

The vehicle routing problem with time windows is frequently found in literature, while multiple time windows are not often considered. In this paper a mathematical formulation of the vehicle routing problem with multiple time windows is presented, taking into account periodic constraints. A meta-heuristic based on Ant Colony System is proposed and implemented. Computational results related to a purpose-built benchmark are finally reported

Improved understanding of the searching behavior of ant colony optimization algorithms applied to the water distribution design problem

[1] Evolutionary algorithms (EAs) have been applied successfully to many water resource problems, such as system design, management decision formulation, and model calibration. The performance of an EA with respect to a particular problem type is dependent on how effectively its internal operators balance the exploitation/exploration trade-off to iteratively find solutions of an increasing quality. For a given problem, different algorithms are observed to produce a variety of different final performances, but there have been surprisingly few investigations into characterizing how the different internal mechanisms alter the algorithm’s searching behavior, in both the objective and decision space, to arrive at this final performance. This paper presents metrics for analyzing the searching behavior of ant colony optimization algorithms, a particular type of EA, for the optimal water distribution system design problem, which is a classical NP-hard problem in civil engineering. Using the proposed metrics, behavior is characterized in terms of three different attributes: (1) the effectiveness of the search in improving its solution quality and entering into optimal or near-optimal regions of the search space, (2) the extent to which the algorithm explores as it converges to solutions, and (3) the searching behavior with respect to the feasible and infeasible regions. A range of case studies is considered, where a number of ant colony optimization variants are applied to a selection of water distribution system optimization problems. The results demonstrate the utility of the proposed metrics to give greater insight into how the internal operators affect each algorithm’s searching behavior.A.C. Zecchin, A.R. Simpson, H.R. Maier, A. Marchi and J.B. Nixo

To Innovate or Not To Innovate?

Reimann M, Bullnheimer B, Dawid H. To Innovate or Not To Innovate? IEEE Transactions on Evolutionary Computation (special ussue on Agent-based Computational Economics). 2001;5(5):471-481.n this paper, we analyze the evolution of output decisions of adaptive firms in an environment of oligopolistic competition. The firm might either choose to produce one of several existing product variants or try to establish a new product variant on the market. The demand for each individual product variant is subject to a life cycle, but aggregate demand for product variants is constant over time. Every period each firm has to decide whether to produce the product again, introduce a new product variant itself (which generates an initial advantage on that market), or follow another firm and change to the production of an already established product. Different firms have heterogeneous abilities to develop products and imitate existing designs; therefore, the effects of the decision whether to imitate existing designs or to innovate differ between firms. We examine the evolution of behavior in this market using an agent-based simulation model. The firms are endowed with simple rules to estimate market potentials and market founding potentials of all firms, including themselves, and make their decisions using a stochastic learning rule. Furthermore, the characteristics of the firms change dynamically due to “learning by doing” effects. The main questions discussed are how the success and the optimal strategy of a firm depend on the interplay between characteristics of the industry and properties of the fir

Learning from Own and Foreign Experience: Technological Adaptation by imitating Firms

Bullnheimer B, Dawid H, Zellner R. Learning from Own and Foreign Experience: Technological Adaptation by imitating Firms. Computational & Mathematical Organization Theory. 1998;4(3):267-282.n this paper we study the adaptive behavior of firms which repeatedly have to make a production decision. In a single good market the firms use own experience as well as information gathered by observing competitors to iteratively choose a production technology out of a given set. The adaptive learning of the firms is described in a dynamic model and analyzed in a simulation framework. We show that a small but positive propensity to imitate is optimal for the firms and yields production efficiencies above 95% of the maximal value. Furthermore, we observe that in a competitive situation firms using optimal propensities to imitate outmatch pure imitators and nonimitators in production efficiency as well as in profits

and Management Science’). To Innovate or Not To Innovate £

In this paper we analyze the evolution of output decisions of adaptive firms in an environment of oligopolistic competition. The firm might either choose to produce one of several existing product variants or try to establish a new product variant on the market. The demand for each individual product variant is subject to a life-cycle, but aggregate demand for product variants is constant over time. Every period each firm has to decide whether to produce the product again, to introduce a new product variant itself (which generates an initial advantage on that market), or to follow another firm and change to the production of an already established product. Different firms have heterogeneous abilities to develop products respectively imitate existing designs, and therefore the effects of the decision whether to imitate existing designs or to innovate differ between firms. We examine the evolution of behavior in this market using an agent based simulation model. The firms are endowed with simple rules to estimate market potentials and market founding potentials of all firms including themselves, and make their decisions using a stochastic learning rule. Furthermore, the characteristics of the firms change dynamically due to ’learning by doing ’ effects. The main questions discussed are how the success and the optimal strategy of a firm depend on the interplay between characteristics of the industry and properties of the firm.

Learning from Own and Foreign Experience: Technological Adaptation by Imitating Firms

In this paper we study the adaptive behavior of firms which repeatedly have to make strategic production decisions. In a single good market the firms use own experience as well as information gathered by observing competitors to iteratively choose a technology out of a given set. The adaptive learning of the firms is analyzed in a dynamic model formally resembling the so called 'Ant System'. In several simulations we show that a small but positive propensity to imitate is optimal for the firms yielding production efficiencies above 95% of the maximal value. Furthermore, we observe that in a competitive situation firms using optimal propensities to imitate outmatch pure imitators and non-imitators as well in production efficiency as in profits