By Christine Solnon
Ant colony optimization is a metaheuristic which has been effectively utilized to a variety of combinatorial optimization difficulties. the writer describes this metaheuristic and reports its potency for fixing a few difficult combinatorial difficulties, with a particular specialise in constraint programming. The textual content is equipped into 3 parts.
The first half introduces constraint programming, which gives excessive point good points to declaratively version difficulties via constraints. It describes the most present ways for fixing constraint delight difficulties, together with whole tree seek ways and metaheuristics, and exhibits how they are often built-in inside constraint programming languages.
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Extra resources for Ant Colony Optimization and Constraint Programming
Other problems, such as maximum clique, cannot be approximated with a constant factor or even with a polynomial factor (unless P = N P). These -approximation algorithms are not described in this book. We refer the interested reader to [SHM 95] for more details. 3. Heuristics and metaheuristics A third possibility of avoiding combinatorial explosion is to partially explore the search space by deliberately choosing to ignore some parts of it. These approaches are therefore said to be incomplete and use heuristics and metaheuristics to guide the search when choosing the combinations to be explored: – heuristics are empirical rules which depend on the problem to be solved; and – metaheuristics are generic strategies that may be used to solve any combinatorial optimization problem.
4. info 22 ACO and CP avoided. However, we can easily design an algorithm to make the problem appear less tricky. To better understand the type of challenge we have to take up, let us consider a problem which may be solved by a simple review of 2n combinations where n is the size of the input data. 8. 1, we may generate all possible truth assignments until finding an assignment which satisfies the formula. If the instance to be solved has n variables, there exist 2n truth assignments as each variable may be assigned to either true or false.
However, many real world problems are N P-hard such as timetabling, load balancing of assembly lines, vehicle routing and frequency assignment. html) reviews more than 100 of these problems. It is therefore important to design approaches that are able to solve these problems in practice. To achieve this aim, we may consider the alternatives which are described in the following sections. 1. Exploitation of particular cases Many N P-complete problems become polynomial when the input data of the instance to solve satisfies some properties.
Ant Colony Optimization and Constraint Programming by Christine Solnon