PLA Based Strategy for Solving RCPSP by a Team of Agents

In this paper the dynamic interaction strategy based on the Population Learning Algorithm (PLA) for the A-Team solving the Resource-Constrained Project Scheduling Problem (RCPSP) is proposed and experimentally validated. The RCPSP 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

PLA Based Strategy for Solving RCPSP by a Team of Agents

In this paper the dynamic interaction strategy based on the Population Learning Algorithm (PLA) for the A-Team solving the Resource-Constrained Project Scheduling Problem (RCPSP) is proposed and experimentally validated. The RCPSP 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