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Swarm Intelligence
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Swarm intelligence (SI) originated from the study of colonies, or swarms of social organisms Studies of the social behavior of organisms (individuals) in swarms prompted the design of very e cient optimization and clustering algorithms For example, simulation studies of the graceful, but unpredictable, choreography of bird ocks led to the design of the particle swarm optimization algorithm, and studies of the foraging behavior of ants resulted in ant colony optimization algorithms Particle swarm optimization (PSO) is a stochastic optimization approach, modeled on the social behavior of bird ocks PSO is a population-based search procedure where the individuals, referred to as particles, are grouped into a swarm Each particle in the swarm represents a candidate solution to the optimization problem In a PSO system, each particle is own through the multidimensional search space, adjusting its position in search space according to its own experience and that of neighboring particles A particle therefore makes use of the best position encountered by itself and the best position of its neighbors to position itself toward an optimum solution The e ect is that particles y toward an optimum, while still searching a wide area around the current best solution The performance of each particle (ie the closeness of a particle to the global minimum) is measured according to a prede ned tness function which is related to the problem being solved Applications of PSO include function approximation, clustering, optimization of mechanical structures, and solving systems of equations Studies of ant colonies have contributed in abundance to the set of intelligent algorithms The modeling of pheromone depositing by ants in their search for the shortest paths to food sources resulted in the development of shortest path optimization algorithms Other applications of ant colony optimization include routing optimization in telecommunications networks, graph coloring, scheduling and solving the quadratic assignment problem Studies of the nest building of ants and bees resulted in the development of clustering and structural optimization algorithms
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Arti cial Immune Systems
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The natural immune system (NIS) has an amazing pattern matching ability, used to distinguish between foreign cells entering the body (referred to as non-self, or antigen) and the cells belonging to the body (referred to as self) As the NIS encounters antigen,
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1 Introduction to Computational Intelligence
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the adaptive nature of the NIS is exhibited, with the NIS memorizing the structure of these antigen for faster future response the antigen In NIS research, four models of the NIS can be found: The classical view of the immune system is that the immune system distinguishes between self and non-self, using lymphocytes produced in the lymphoid organs These lymphocytes learn to bind to antigen Clonal selection theory, where an active B-Cell produces antibodies through a cloning process The produced clones are also mutated Danger theory, where the immune system has the ability to distinguish between dangerous and non-dangerous antigen Network theory, where it is assumed that B-Cells form a network When a B-Cell responds to an antigen, that B-Cell becomes activated and stimulates all other B-Cells to which it is connected in the network An arti cial immune system (AIS) models some of the aspects of a NIS, and is mainly applied to solve pattern recognition problems, to perform classi cation tasks, and to cluster data One of the main application areas of AISs is in anomaly detection, such as fraud detection, and computer virus detection
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Fuzzy Systems
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Traditional set theory requires elements to be either part of a set or not Similarly, binary-valued logic requires the values of parameters to be either 0 or 1, with similar constraints on the outcome of an inferencing process Human reasoning is, however, almost always not this exact Our observations and reasoning usually include a measure of uncertainty For example, humans are capable of understanding the sentence: Some Computer Science students can program in most languages But how can a computer represent and reason with this fact Fuzzy sets and fuzzy logic allow what is referred to as approximate reasoning With fuzzy sets, an element belongs to a set to a certain degree of certainty Fuzzy logic allows reasoning with these uncertain facts to infer new facts, with a degree of certainty associated with each fact In a sense, fuzzy sets and logic allow the modeling of common sense The uncertainty in fuzzy systems is referred to as nonstatistical uncertainty, and should not be confused with statistical uncertainty Statistical uncertainty is based on the laws of probability, whereas nonstatistical uncertainty is based on vagueness, imprecision and/or ambiguity Statistical uncertainty is resolved through observations For example, when a coin is tossed we are certain what the outcome is, while before tossing the coin, we know that the probability of each outcome is 50% Nonstatistical uncertainty, or fuzziness, is an inherent property of a system and cannot be altered or resolved by observations Fuzzy systems have been applied successfully to control systems, gear transmission
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