Abstract
The design of real-world engineering systems deals with high complexity at the system and subsystem levels, as well as the interaction between the subsystems involved. Multidisciplinary Design Optimization (MDO), which aims to integrate the optimization algorithms in both system and subsystem levels along with exploring mechanisms of synergistic action among subsystems, is a renowned methodology to design complex engineering systems. In Multi-Agent Systems (MASs), as a complex system, to achieve the best system performance, agents have to work their individual tasks in a coordinated manner and in cooperation with other agents. Given some architectural resemblance between MDO and MAS, especially in subsystems interaction (agents, in MAS case), this paper addresses proposing a multi-agent multidisciplinary system design optimization strategy. This strategy by taking advantage of the capabilities of MDO proposes a consolidated framework for the optimal design of real world engineering systems. This strategy is extendable to multi-agent systems in a diverse range of missions and tasks. In this regard to illustrating its application a distributed cooperative search and coverage (CSC) mission in uncertain environments is considered as a case study for designing a MAS. The proposed framework contributes to designing a MAS by applying the history data of previously completed missions and the normalized cross-correlating (NCC) algorithm as well. The effectiveness of the proposed framework is demonstrated via simulations and comparison.