Download Agent-Based Optimization by Ireneusz Czarnowski, Piotr Jędrzejowicz, Janusz Kacprzyk PDF

By Ireneusz Czarnowski, Piotr Jędrzejowicz, Janusz Kacprzyk

This quantity provides a set of unique learn works via prime experts targeting novel and promising techniques within which the multi-agent procedure paradigm is used to help, increase or change conventional techniques to fixing tricky optimization difficulties. The editors have invited a number of recognized experts to provide their suggestions, instruments, and types falling lower than the typical denominator of the agent-based optimization. The booklet includes 8 chapters overlaying examples of software of the multi-agent paradigm and respective personalized instruments to unravel tough optimization difficulties coming up in numerous components similar to computing device studying, scheduling, transportation and, extra usually, disbursed and cooperative challenge fixing.

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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). – In a distributed approach — each processor has its own pheromone matrix, which is updated based on information obtained from other processors.

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.

AVNProc begin Initialize; foreach loop do Locate Ants; foreach iteration do foreach ant do if ant is active then Construct Probability; Select Route; Update TABU List; Kill blocked ant; Value Ants; Award Winner Ants; Punish Loser Ants; Evaporate Pheromone; Select Best Optimized Direction; and cost functions for all parameters for edge ij are: ξi jdistance , ξi jwidth , ξi jtra f f ic , ξi jrisk and ξi jquality . Based on the calculated probability ant k selects a route to go. 1 , to choose an exploitation or an exploration: j= arg max{pkih } if q ≤ Q (exploitation) h∈Jik S (5) otherwise (exploration) In next steps the node selected by the ant is added to its TABU list and if ant k arrives the destination or is blocked at the certain node, it is deleted from the active ant list.

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