|Article title||SWARMS ALGORITHMS OF SEARCH OPTIMIZATION BASED ON MODELING THE BEHAVIOR OF BATS|
|Authors||E.V. Kuliev, A.A. Lezhebokov, Yu.A. Kravchenko|
|Section||SECTION II. BIOINSPIRED SEARCH|
|Month, Year||07, 2016 @en|
|Abstract||The paper discusses to solve of the key artificial intelligence problem – development of new effective heuristic search mechanisms. As the most perspective directions in search optimization theory is considered principles and rules of species behavior in nature for solving NP-hard optimization problems. The most interesting are methods and algorithms based on multiagent management and swarm intelligent. The paper deals with swarm algorithm based on simulation of bats’ behavior. The model of one specie’s behavior is a simulation of it’s movement within limited space to find a quasi optimal solution. There are represented several approaches to manage the algorithm configurations – frequency and range of echo signals, probabilistic rules for changing the direction of movement. The authors developed a flowchart of algorithm for the VLSI single-sized elements placement problem. An unified approach for solution encoding as a string allow to use the developed algorithm for various initial data and restrictions of the placement problem. Initial area of the search space is formed by sequential, random and iterative algorithms. There is a software on the basis of object oriented language. A set of experimental computations were carried out to confirm the algorithm time complexity and it efficiency. The complexity of the swarm algorithm based on bats’ behavior depends on a number of species linearly and has a quadratic character of dependency on dimensionality of initially problem, i.e. on a number of placed elements which denotes a solutions’ string length.|
|Keywords||Placement; adaptive procedures; evolution simulation; swarm algorithm; genetic algo-rithm; bats; software.|
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