By Ireneusz Czarnowski, Piotr Jędrzejowicz, Janusz Kacprzyk
This quantity provides a suite of unique study works through major experts targeting novel and promising techniques within which the multi-agent process paradigm is used to help, improve or change conventional ways to fixing tricky optimization difficulties. The editors have invited numerous famous experts to give their strategies, instruments, and versions falling lower than the typical denominator of the agent-based optimization. The e-book involves 8 chapters masking examples of program of the multi-agent paradigm and respective custom-made instruments to unravel tricky optimization difficulties bobbing up in several components corresponding to laptop studying, scheduling, transportation and, extra in general, disbursed and cooperative challenge fixing.
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Extra resources for Agent-Based Optimization
Sometimes it puts a lot of ants on a single processor treating them as a colony of cooperating ants. • When assessing the quality of solutions, they are compared with the solutions obtained by the standard versions of the ACO, which are run independently on different processors, perhaps with different parameter values. g. exchanging information between processors less than after each iteration, which by the way causes the colonies to search different regions of the solution space). ACO for the Vehicle Navigation 41 • Centralized or decentralized approach to a pheromone matrix: – With a centralized approach — selected processor collects information about the solution or pheromone from other processors, updates and distributes pheromone tables to other processors (master-slave principle).
6 presents selected results of experiments with NAVN and PAVN algorithms. 4 GHz). This map consists of 64K nodes and 144K edges and is based on data collected from the system Open Street Map (OSM) for the area of the city of Katowice. The start node OSM id was 262831991 (city of Gliwice, Akademicka street) and end node id was 297573921 (city of O´swie¸cim, Zatorska street). The time of departure was 17:30 and travel speed: 40 km/h. 000025 Table 6 Selected results of experiments with NAVN i PAVN Algorithm Threads Cycles Ants Time [ms] Cost Distance [m] NAVN 1 50 16 20611 2290931 67055 NAVN 1 50 NAVN 1 100 32 36723 2205954 68531 32 49597 2277718 69556 PAVN 16 50 16 22033 2352790 81289 PAVN PAVN 8 50 16 13626 2426367 66374 8 100 16 7766 2668998 83915 PAVN PAVN 8 50 32 13470 2737325 68416 8 100 32 14673 2410930 81990 In experiment only the preferences of the distance were used, other values were constant.
The OCLAVN algorithm may be described as shows procedure OCLAVNProc (procedure RunAnts is invoked for kernel calculations): ACO for the Vehicle Navigation Procedure. OCLAVNProc begin PrepareNormalization; Initialize; Init MemoryOnDevices; foreach device do Write SearchMap; foreach loop do foreach device do InvokeKernel RunAnts for antCount ∗ warpSize threads in antCount groups; foreach device do Read AntSolutions; foreach device do ValueAnts; Modify q0 ; foreach device do Write ChangedParams; if BestSolutionIsChanged then foreach device do Write BestSolution; 45 Procedure.