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16th International KES Conference on Intelligent Decision Technologies

作者： Czarnowski, Ireneusz Gdynia Maritime Univ Morska Str 81-87 Gdynia Poland

ISBN: (纸本)9789819774210;9789819774197;9789819774180

In this paper, we review the population learning algorithm and discuss its convergence. This algorithm was proposed as a tool for solving optimisation problems, and the concept underlying this approach is embedded in social educational processes. A convergence analysis of the algorithm is presented by means of a finite Markov chain analysis and by comparing its behaviour to evolutionary strategies in a process of searching for a global solution in a finite number of stages. The proposed population algorithm is also shown to be an alternative tool for solving different optimisation problems.

关键词： Convergence Evolutionary computation population learning algorithm population-based optimisation tool

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Designing RBF Networks Using the Agent-Based population learning algorithm

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NEW GENERATION COMPUTING 2014年第3-4期32卷 331-351页

作者： Czarnowski, Ireneusz Jedrzejowicz, Piotr Gdynia Maritime Univ Dept Informat Syst PL-81225 Gdynia Poland

Radial Basis Function Neural Networks (RBFNs) are nowadays quite popular due to their ability to discover and approximate complex nonlinear dependencies within the data under analysis. Performance of the RBF network depends on numerous factors related to its initialization and training. The paper proposes an approach to the radial basis function networks design, where initial parameters of the network, output weights and parameters of the transfer function are set using the proposed agent-based population learning algorithm (PLA). The algorithm is validated experimentally. Advantages and main features of the PLA-based RBF designs are discussed basing on results of the computational experiment.

关键词： Neural network RBF networks Evolutionary computations population learning algorithm Multi-agent systems

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A cooperative population learning algorithm for vehicle routing problem with time windows

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NEUROCOMPUTING 2014年 146卷 210-229页

作者： Barbucha, Dariusz Gdynia Maritime Univ Dept Informat Syst PL-81225 Gdynia Poland

population-based hybrid metaheuristics, often inspired by biological or social phenomena, belong to a widely used groups of methods suitable for solving complex hard optimization problems. Their effectiveness has been confirmed for providing good quality solutions to many real-life instances of different problems. Recently, an incorporation of the cooperative problem solving paradigm into metaheuristics has become an interesting extension of the population-based hybrid metaheuristics. Cooperation is meant as a problem-solving strategy, consisting of a search performed by different search agents running in parallel. During the search, the agents cooperate by exchanging information about states, solutions or other search space characteristics. This paper proposes an Agent-Based Cooperative population learning algorithm for the Vehicle Routing Problem with Time Windows. In the proposed approach the process of search for the best solution is divided into stages, and different search procedures are used at each stage. These procedures use a set of various heuristics (represented by software agents) which run under the cooperation scheme defined separately for each stage. Computational experiment which has been carried out, confirmed the effectiveness of the proposed approach. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Computational collective intelligence Cooperative problem solving Agent-based optimization population learning algorithm Vehicle routing problems

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A Cross-Entropy Based population learning algorithm for Multi-mode Resource-Constrained Project Scheduling Problem with Minimum and Maximum Time Lags

A Cross-Entropy Based Population Learning Algorithm for Mult...

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2nd International Conference on Computational Collective Intelligence: Technologies and Applications

作者： Jedrzejowicz, Piotr Skakovski, Aleksander Gdynia Maritime Univ Chair Informat Syst Ul Morska 83F PL-81225 Gdynia Poland Gdynia Maritime Univ Dept Navigat PL-81225 Gdynia Poland

ISBN: (纸本)9783642166921

The multi-mode resource-constrained project scheduling problem with minimum and maximum time lags is considered in the paper. An activity is performed in a mode, which determines the demand of renewable and nonrenewable resources required for its processing and minimum and maximum time lags between adjacent activities. The goal is to find a mode assignment to the activities and their start times such that all constraints are satisfied and the project duration is minimized. Because the problem is NP-hard a population-learning algorithm (PLA2) is proposed to tackle the problem. PLA2 is a population-based approach which takes advantage of the features common to the social education system rather than to the evolutionary processes. The proposed approach perfectly suits for multi-agent systems because it is based on the idea of constructing a hybrid algorithm integrating different optimization techniques complementing each other and producing a synergetic effect. Results of the experiment were compared to the results published in Project Scheduling Problem Library.

关键词： multi-mode resource-constrained project scheduling minimum and maximum time lags evolutionary computation population learning algorithm

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Experimental Evaluation of the Agent-Based population learning algorithm for the Cluster-Based Instance Selection

Experimental Evaluation of the Agent-Based Population Learni...

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3rd International Conference on Computational Collective Intelligence (ICCCI 2011)

作者： Czarnowski, Ireneusz Jedrzejowicz, Piotr Gdynia Maritime Univ Dept Informat Syst PL-81225 Gdynia Poland

ISBN: (纸本)9783642239373;9783642239380

The aim of the paper is to propose and evaluate an agent-based population learning algorithm generating, by the prototype selection, a representative training clataset of the required size. It is assumed that prototypes are selected from clusters. The process of selection is executed by a team of agents, which execute various local search procedures and cooperate to find-out a solution to the considered problem. Rules for agent cooperation are defined within working strategies. In this paper influence of two different strategies and the population size on performance of the algorithm is investigated.

关键词： instance selection population learning algorithm A-Team

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population learning with Differential Evolution for the Discrete-Continuous Scheduling with Continuous Resource Discretisation

Population Learning with Differential Evolution for the Disc...

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IEEE International Conference on Cybernetics (CYBCONF)

作者： Jedrzejowicz, Piotr Skakovski, Aleksander Gdynia Maritime Univ Chair Informat Syst Gdynia Poland Gdynia Maritime Univ Dept Navigat Gdynia Poland

ISBN: (纸本)9781467364690

In the paper, we consider a population learning algorithm denoted (PLA3), with the differential evolution method for solving the discrete-continuous scheduling problem (DCSP) with continuous resource discretisation - Theta(Z). The considered problem originates from DCSP, in which nonpreemtable tasks should be scheduled on parallel identical machines under constraint on discrete resource and requiring, additionally, a renewable continuous resource to minimize the schedule length. The continuous resource in DCSP is divisible continuously and is allocated to tasks from a given interval in amounts unknown in advance. Task processing rate depends on the allocated amount of the continuous resource. To eliminate time consuming optimal continuous resource allocation, an NP-hard problem Theta(Z) with continuous resource discretisation is introduced and sub-optimally solved by PLA3. Experimental results show that PLA3 was able to improve best-known solutions and excels its predecessor PLA2 in solving the considered problem.

关键词： population learning algorithm Differential Evolution Method Discrete-Continuous Scheduling Discretisation

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An Experimental Study of Scenarios for the Agent-Based RBF Network Design 1

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7th KES International Conference on Intelligent Decision Technologies (KES-IDT)

作者： Czarnowski, Ireneusz Jedrzejowicz, Piotr Gdynia Maritime Univ Dept Informat Syst Morska 83 PL-81225 Gdynia Poland

ISBN: (数字)9783319198576

ISBN: (纸本)9783319198576;9783319198569

The paper focuses on the radial basis function neural design problem. Performance of the RBF neural network strongly depends on the network structure and parameters. Making choices with respect to the structure and value of the RBF network parameters involves both stages of its design: initialization and training. The basic question considered in this paper is how these two stages should be carried-out. Should they be carried-out sequentially, in parallel, or perhaps based on other predefined schema or strategy? In the paper an agent-based population learning algorithm is used as a tool for designing of the RBF network. Computational experiment has been planned and executed with a view to investigate effectiveness of different approaches. Experiment results have been analyzed to draw some general conclusions with respect to strategies for the agent-based RBF network design.

关键词： Neural networks RBF network population learning algorithm

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An investigation of agent-based hybrid approach to solve flowshop and job-shop scheduling problems

An investigation of agent-based hybrid approach to solve flo...

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11th International Conference on Knowledge-Based Intelligent Informational and Engineering Systems/17th Italian Workshop on Neural Networks

作者： Jedrzejowicz, Joanna Jedrzejowicz, Piotr Univ Gdansk Inst Comp Sci Wita Stwosza 57 PL-80952 Gdansk Poland Gdynia Maritime Univ Dept Informat Syst PL-81225 Gdynia Poland

ISBN: (纸本)9783540748175

The paper investigates a possibility of combining the population learning algorithm and the A-Team concept with a view to increase quality of results and efficiency of computations. To implement the idea a middleware environment called JABAT is used. The proposed approach is validated experimentally using benchmark datasets containing instances of the two well-known combinatorial optimization problems: flow shop and job shop scheduling.

关键词： population learning algorithm A-Team flow shop and job shop scheduling

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Agent-Based Data Reduction Using Ensemble Technique

Agent-Based Data Reduction Using Ensemble Technique

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5th International Conference on Computational Collective Intelligence (ICCCI)

作者： Czarnowski, Ireneusz Jedrzejowicz, Piotr Gdynia Maritime Univ Dept Informat Syst PL-81225 Gdynia Poland

ISBN: (纸本)9783642404955;9783642404948

The problem addressed in this paper concerns data reduction. In the paper the agent-based data reduction algorithm is extended by adding mechanism of integration of the multiple learning models into a single multiple classification system called ensemble model. The paper includes the overview of the proposed approach and discusses the computational experiment results.

关键词： learning from data data reduction ensemble population learning algorithm multi-agent system

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PLA Based Strategy for Solving MRCPSP by a Team of Agents 8th

PLA Based Strategy for Solving MRCPSP by a Team of Agents

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8th KES International Conference on Intelligent Decision Technologies (KES-IDT)

作者： Jedrzejowicz, Piotr Ratajczak-Ropel, Ewa Gdynia Maritime Univ Chair Informat Syst Morska 83 PL-81225 Gdynia Poland

ISBN: (纸本)9783319396309;9783319396293

In this paper the dynamic interaction strategy based on the population learning algorithm (PLA) for the A-Team solving the Multi-mode Resource-Constrained Project Scheduling Problem (MRCPSP) is proposed and experimentally validated. The MRCPSP belongs to the NP-hard problem class. To solve this problem a team of asynchronous agents (A-Team) has been implemented using multiagent system. An A-Team is the set of objects including multiple agents and the common memory which through interactions produce solutions of optimization problems. These interactions are usually managed by some static strategy. In this paper the dynamic learning strategy based on PLA is suggested. The proposed strategy supervises interactions between optimization agents and the common memory. To validate the proposed approach computational experiment has been carried out.

关键词： Multi-mode resource-constrained project scheduling MRCPSP Optimization Agent A-team population learning algorithm

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