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A particle swarm searching for the global minimum of a function. In computational science, particle swarm optimization (PSO) [1] is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality.
As a result of the completion of the last iteration, a system will be obtained that can correctly determine the landmark with a certain accuracy. In the particle swarm optimization method, there are particles that search for landmarks, and each of them uses a certain formula in each iteration to optimize landmark detection. [15]
Multi-swarm optimization is a variant of particle swarm optimization (PSO) based on the use of multiple sub-swarms instead of one (standard) swarm. The general approach in multi-swarm optimization is that each sub-swarm focuses on a specific region while a specific diversification method decides where and when to launch the sub-swarms.
This algorithm is a member of the ant colony algorithms family, in swarm intelligence methods, and it constitutes some metaheuristic optimizations. Initially proposed by Marco Dorigo in 1992 in his PhD thesis, [ 6 ] [ 7 ] the first algorithm was aiming to search for an optimal path in a graph, based on the behavior of ants seeking a path ...
2005 DARPA Grand Challenge winner Stanley performed SLAM as part of its autonomous driving system. A map generated by a SLAM Robot. Simultaneous localization and mapping (SLAM) is the computational problem of constructing or updating a map of an unknown environment while simultaneously keeping track of an agent's location within it.
Swarm-based optimization algorithms (e.g., particle swarm optimization, social cognitive optimization, multi-swarm optimization and ant colony optimization) Memetic algorithms, combining global and local search strategies; Reactive search optimization (i.e. integration of sub-symbolic machine learning techniques into search heuristics)
Davie M. proposed using the particle swarm optimization algorithm to solve the multiple sequence alignment problem; Ikeda Takahiro proposed a heuristic algorithm which is based on the A* search algorithm; E. Birney first proposed using the hidden Markov model to solve the multiple sequence alignment problem; and many other biologists use the ...
Particle swarm optimization (PSO) is a global optimization algorithm for dealing with problems in which a best solution can be represented as a point or surface in an n-dimensional space. Hypotheses are plotted in this space and seeded with an initial velocity , as well as a communication channel between the particles.