By Ireneusz Czarnowski, Piotr Jędrzejowicz, Janusz Kacprzyk
This quantity provides a set of unique study works by means of prime experts concentrating on novel and promising ways within which the multi-agent process paradigm is used to help, increase or exchange conventional ways to fixing tricky optimization difficulties. The editors have invited numerous famous experts to offer their ideas, instruments, and types falling less than the typical denominator of the agent-based optimization. The ebook includes 8 chapters overlaying examples of software of the multi-agent paradigm and respective custom-made instruments to unravel tricky optimization difficulties bobbing up in several components akin to computing device studying, scheduling, transportation and, extra in general, dispensed and cooperative challenge fixing.
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Extra info for Agent-Based Optimization
The problem is known from the literature as Multi-criteria Shortest Path Problem, and it is proven to be NP-complete . For multi-criteria combinatorial problems a single solution will very seldom be able to minimize (or maximize) all criteria, but rather there will be a set of compromise solutions. These solutions are called efficient non-dominated ones and are also referred to as Pareto optimal set. pl Corresponding author. I. Czarnowski et al. ): Agent-Based Optimization, SCI 456, pp. 29–53.
Using Multi-agent Systems and Consensus Methods for Information Retrival in Internet. International Journal Itelligence Information and Databases Systems 1(2), 181–198 (2007) 72. : A Learning Classifier System with Mutual-information-based Fitness. Evolutionary Intelligence 1(3), 31–50 (2010) 73. : JAM: Java Agentsfor Metalearning over Distributed Databases. In: 3rd International Conference on Knowledge Discovery and Data Mining, pp. 74–81. AAAI Press, NewportBeach (1997) 74. : Reinforcement Learning.
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. RunAnts begin threadId = GetLocalThreadId; antNum = GetGroupId; if threadId == 0 then CopyGlobalParamsToLocalMemory; InitializeLocalAnt; Barrier; InitializeTabuList; foreach iteration do if antIsActive then ConstructProbability; if threadId == 0 then if antHasNoMove then MoveBack; else SelectRoute; UpdateTabuList; Barrier; CopyLocalAntToGlobalMemory; foreach device do InvokeKernel AwardBestSolution for bestSolutionStepCount threads; FreeMemoryOnDevices; SelectBestOptimizedDirection; In the OCLAVN algorithm’s pseudo-code special comment should be provided for keywords Write, Read and InvokeKernel: Write realizes memory transfer from host to global memory on device, Read — memory transfer from global memory on device to host memory, and InvokeKernel runs given procedure on device by given number of threads (work items) divided into groups (work groups).