Download E-books Ant Colony Optimization and Constraint Programming PDF

By Christine Solnon

Ant colony optimization is a metaheuristic which has been effectively utilized to quite a lot of combinatorial optimization difficulties. the writer describes this metaheuristic and reviews its potency for fixing a few difficult combinatorial difficulties, with a particular specialize in constraint programming. The textual content is equipped into 3 components.

The first half introduces constraint programming, which supplies excessive point good points to declaratively version difficulties via constraints. It describes the most current methods for fixing constraint pride difficulties, together with entire tree seek methods and metaheuristics, and exhibits how they are often built-in inside constraint programming languages.

The moment half describes the ant colony optimization metaheuristic and illustrates its functions on various constraint delight problems.
The 3rd half exhibits how the ant colony could be built-in inside a constraint programming language, therefore combining the expressive strength of constraint programming languages, to explain difficulties in a declarative manner, and the fixing strength of ant colony optimization to successfully clear up those problems.

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Five. 2. 1. simple ideas . . . . . . . five. 2. 2. Metaheuristics in keeping with LS five. 2. three. utilizing LS to unravel CSPs . . five. three. Particle swarm optimization . . . five. three. 1. uncomplicated rules . . . . . . . five. three. 2. utilizing PSO to unravel CSPs . five. four. dialogue . . . . . . . . . . . . . 70 70 seventy three seventy three seventy three seventy five seventy seven seventy eight seventy eight eighty eighty bankruptcy 6. positive Heuristic techniques . . . . . . . . . . . . eighty five 6. 1. grasping randomized techniques . . . . . . . . . . . . . . . 6. 1. 1. uncomplicated rules . . . . . . . . . . . . . . . . . . . . . 6. 1. 2. utilizing grasping randomized algorithms to unravel CSPs 6. 2. Estimation of distribution algorithms . . . . . . . . . . . . 6. 2. 1. easy ideas . . . . . . . . . . . . . . . . . . . . . 6. 2. 2. utilizing EDAs to resolve CSPs . . . . . . . . . . . . . . 6. three. Ant colony optimization . . . . . . . . . . . . . . . . . . . 6. four. dialogue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 86 88 88 88 ninety ninety ninety one bankruptcy 7. Constraint Programming Languages . . . . . . . . . . . ninety three Constraint good judgment programming . . Constraint programming libraries Constraint-based neighborhood seek . . dialogue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . half II. A NT C OLONY O PTIMIZATION . . . . . . . . . . . . . . . a hundred and one creation to half II . . . . . . . . . . . . . . . . . . . . . . . . . . 103 bankruptcy eight. From Swarm Intelligence to Ant Colony Optimization one hundred and five www. it-ebooks. details . . . . . . . . . . . . . . . . . . . . ninety four ninety six ninety six ninety nine . . . . . . . . . . . . . . . . . . . . . . . . eight. 1. advanced structures and swarm intelligence . . . . . . eight. 2. looking for shortest paths via ant colonies . . . . . eight. three. Ant procedure and the touring salesman challenge . . . eight. three. 1. Pheromone constitution . . . . . . . . . . . . . . . eight. three. 2. development of a Hamiltonian cycle via an ant . . . . . . . . . . . . . . . sixty nine . . . . . . . . . . . 7. 1. 7. 2. 7. three. 7. four. . . . . . . . . . . . vii . . . . . . . . . . . . . . . . . . . 106 108 111 112 114 viii ACO and CP eight. three. three. Pheromone updating step . . . . . . . . . . . eight. three. four. synthetic as opposed to actual ants . . . . . . . . . . . . eight. four. usual ACO framework . . . . . . . . . . . . . . . eight. four. 1. Pheromone constitution and development graph eight. four. 2. development of mixtures via ants . . . . eight. four. three. bettering combos with neighborhood seek . . eight. four. four. Pheromone updating step . . . . . . . . . . . eight. four. five. Parameters of an ACO set of rules . . . . . . . . . . . . . . . one hundred fifteen a hundred and fifteen 116 116 118 one hundred twenty 121 122 bankruptcy nine. Intensification as opposed to Diversification . . . . . . . . . . . a hundred twenty five nine. 1. ACO mechanisms for intensifying the quest nine. 2. ACO mechanisms for diversifying the hunt nine. three. Balancing intensification and diversification . nine. four. Measures of diversification/intensification . . nine. four. 1. The λ-branching issue . . . . . . . . . . nine. four. 2. Resampling ratio . . . . . . . . . . . . . nine. four. three. Similarity ratio . . . . . . . . . . . . . . bankruptcy 10. past Static Combinatorial difficulties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . one hundred twenty five 127 128 a hundred thirty five 136 136 137 . . . . . . . . 141 10. 1. Multi-objective difficulties . . . . . . . . . . . . . . . . . . . . 10. 1. 1. Definition of multi-objective difficulties . . . . . . . . . 10. 1. 2. fixing multi-objective issues of ACO . . . . . 10. 2. Dynamic optimization difficulties . . . . . . . . . . . . . . . . 10. 2. 1. Definition of dynamic optimization difficulties . . . . . 10. 2. 2. fixing dynamic optimization issues of ACO . . 10.

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