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Real parameter optimization is an important task in almost all engineering applications. This paper introduces a novel multiagent architecture and agent interaction mechanism for the solution of single objective type realparameter optimization problems. The proposed multiagent system includes several metaheuristics as problem-solving agents that act on a common population containing the frontiers of search process and a common archive keeping the promising solutions extracted so far. Each session of the proposed architecture includes two phases: a tournament among all agents to determine the currently best performing agent and a search procedure conducted by the winner. In the tournament phase, each agent performs a fixed number of fitness evaluations over the common population and gets a success score in terms the fitness improvements it achieved by itself. The agent with the best score is the winner of the tournament. Then, the winner agent is allowed to conduct its search algorithm using the common population until its procedure gets stuck at a locally optimal solution or maximum fitness evaluations per session is reached. Afterwards, the procedure restarts with another tournament to determine the next winner. In all phases and iterations of the proposed framework, all agents use the same population and archive in conducting their search procedures. This way, agents compete with each other in terms of their fitness improvements achieved over a fixed number of fitness evaluations in tournaments, and Communicated by V. Loia.B Adnan Acan they cooperate by sharing their search experiences through accumulating them in a common population and a common archive. The proposed multiagent system is experimentally evaluated using the well-known CEC2005 benchmark problems set. Analysis of the obtained results exhibited that the proposed framework performs significantly better than its state-of-the-art competitors in almost all problem instances.

Data allocation problem (DAP) in distributed database systems is a NP-hard optimization problem with significant importance in parallel processing environments. The solution of problem aims to minimize the total cost of transactions and settlement of queries in which he main cost regards to the data transmission through the distributed system. These costs are affected by the strategy how to allocate fragments to the sites. Researchers have been solving this challenging problem by applying soft computing methods especially evolutionary algorithms. This study proposes a novel hybrid method based on differential evolution (DE) algorithm and variable neighborhood search (VNS) mechanism for solving DAP problem. The suggested hybrid method (DEVNS) aims to increase the performance of DE algorithm by applying effective selection and crossover operators. Moreover, DEVNS goals to improve the solutions found so far using VNS technique. By applying VNS, more promising parts of search space can be extracted. Eventually, the introduced DEVNS explores the search space via DE and fulfills more exploitation by neighborhood search mechanism. Performance of proposed DEVNS is experimentally evaluated against nine state-of-the-art methods using well-known benchmarks reported in literature. Obtained results exhibits that proposed DEVNS takes the first position for 13 of 20 test problems. Likewise, Friedman aligned rank test is carried out to demonstrate that there is significant statistical difference between all methods.

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