Dinamik komşuluklu eşzamansız dağınık parçacık sürü eniyileme yöntemi ve çok robotlu arama görevinde uygulanması
In this thesis decentralized asynchronous particle swarm optimization (PSO) with dynamic neighborhood topology and its implementation to a search task of a multi-agent system were studied. Particle swarm optimization with dynamic neighborhood topology studied in this thesis allows the neighbors of particles or basically the neighborhood topology to change dynamically with time. Such a view of the algorithm is advantageous for its parallel and distributed implementations. The algorithm with dynamic neighborhood topology was tested on various benchmark functions under different neighborhood dynamics. Decentralized asynchronous realization of particle swarm optimization algorithm is suitable for parallel and distributed implementations. Such a version of the algorithm allows particles to exchange information and update their estimates at totally independent time instants and dynamically change neighborhood topology of particles with respect to time. Simulations were performed using a single processor and multiple processors in a computer network. The proposed algorithm is used for a search task of a multi-agent system in an unknown environment which consist of small robots with limited sensing capability. The method adopts asynchronous mechanism for information exchange and position updates of the agents and dynamic neighborhood topology of communicating agents. Simulations with a realistic simulator and implementation with real robots were performed to show the effectiveness of the proposed algorithm.