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Deliberate though unscienti c observations reveals that management often grows tired of catalysts that manifest in programs, initiatives, projects, and slogans before they actually catch on It takes time for people and organizations to grasp their intent, and by the time they do, management has moved on They either move on physically to another station, or they simply change their minds The hazard from changing in midstream was revealed in studies performed by a former colleague, Michael Reiman PhD, who determined that attempts at implementing concurrent engineering strategy without following through to completion produced worse results than if the organization had not embarked upon change at all Often, to realize the bene t, organizations must con rm that they have the capacity for change and improvement that is a combination of capital, material, and intellectual resources, including management and technical What capacity will it take to change from their present intent to actually adopting a smart data strategy This book provides a construct for answering this question Of course, there are numerous examples in management science history about CEOs who lead with initiatives aimed at improving quality and service, and reducing costs Often, CEOs are engineers who introduce a degree of science and math to their initiatives, principally aimed at desired metrics and outcomes Today, the more popular catalysts do this such as Six Sigma and Lean Efforts such as CMMI aim at improvement from processes and continuous process improvement Balanced Score Card addresses a mix of dynamics with corresponding elements in organization strategy The more complicated catalysts are, the least likely they are to become sustainable They burn out too soon One reason for this is organizations have limited capacity for change and improvement Leaders must select strategies that can be accomplished by organizations within the bounds of certain resource constraints Our natural inclination is to change the subject every so often because we get bored We sometimes confuse overcoming boredom with a quest for continuous improvement A better way to regulate our inclination to move forward is to insist on accountability for milestone achievements as a prerequisite Commercial enterprise must pursue becoming best at what s new Government enterprise must optimize enterprise performance and leverage the most from commercial trading partners That is why we devote considerable attention to the sources of best technologies to determine what they offer to implementing smart data In government, it is interesting because leaders are encouraged to embrace best commercial practices and commercial-off-the-shelf technologies for which the metrics aim at maximizing pro ts for shareholders, which is quite different from government s desire to maximize service utility for citizens Government leaders have predictably short tenures that accompany planning and budget cycles that exacerbate the short time in which they have to apply their talent.

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The magnitude of problems and needs, and their associated life cycles are large and long respectively Do the catalysts used by government executives possess characteristics best suited for their tentative tenure We don t have all the answers to this question, although by being aware, we can factor this into our recommendations about strategy to improve performance JAMES A GEORGE JAMES A RODGER

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Ants appeared on earth some 100 million years ago, and have a current total population estimated at 1016 individuals [378] It is further estimated that the total weight of ants is in the same order of magnitude as the total weight of human beings Most of these ants are social insects, living in colonies of 30 to millions of individuals Ants are not the only social insects living in colonies The complex behaviors that emerge from colonies of ants have intrigued humans, and there have been many studies of ant colonies aimed at a better understanding of these collective behaviors Collective ant behaviors that have been studied include the foraging behavior, division of labour, cemetery organization and brood care, and construction of nests The South African, Eug ne Marais (1872-1936) was one of the rst to study termite colonies He published e his observations as early as 1927 in local magazines and newspapers In his book, The Soul of the Ant [558] ( rst published in 1937, after his death), he described in detail his experimental procedures and observations of the workings of termite societies The Belgian, Maurice Maeterlinck (1862 1949), published The Life of the White Ant [547], which was largely drawn from Marais s articles (see the discussion in [558]) Following on form this pioneering work, the French biologist Pierre-Paul Grass [333] e postulated on the mechanics of termite communication in his studies of their nest construction behavior Grass determined that a form of indirect communication exists e between individuals, which he termed stigmergy It is through this local interaction between individuals, and between individuals and the environment, that more complex behaviors emerges More recently, Deneubourg et al [199] studied one example of stigmergy, namely pheromonal communication From these studies, the rst algorithmic models of foraging behavior have been developed and implemented [208] While most research e orts concentrated on developing algorithmic models of foraging behavior, models have been developed for other behaviors, including division of labour, cooperative support, self-assembly, and cemetery organization These complex behaviors emerge from the collective behavior of very unsophisticated individuals In the context of collective behaviour, social insects are basically stimulus response agents Based on information perceived from the local environment, an individual performs a simple, basic action These simple actions appear to have a large random component Despite this simplicity in individual behavior, social insects form a highly structured social organism To illustrate the complexity, and structured organization of ant colonies, Marais [558] pointed out the resemblance between the human body and a termite society.

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Computational Intelligence: An Introduction, Second Edition AP Engelbrecht c 2007 John Wiley & Sons, Ltd

This chapter provides an introductory overview of ant algorithms Section 171 considers the foraging behavior of ants and discusses the rst ant algorithms developed on the basis of foraging models to solve discrete combinatorial optimization problems Models of the cemetery organization and brood care behaviors are discussed in Section 172 The division of labor behavior is covered in Section 173 Some advanced topics are presented in Section 174, including application of ACO in continuous environments, MOO, dynamic environments, and handling constraints Applications of ant algorithms (AA) are summarized in Section 175

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