Multiagent cooperation for solving global optimization problems: an extendible framework with example cooperation strategies

This paper proposes the use of multiagent cooperation for solving global optimization problems through the introduction of a new multiagent environment, MANGO. The strength of the environment lays in itsflexible structure based on communicating software agents that attempt to solve a problem cooperatively. This structure allows the execution of a wide range of global optimization algorithms described as a set of interacting operations. At one extreme, MANGO welcomes an individual non-cooperating agent, which is basically the traditional way of solving a global optimization problem. At the other extreme, autonomous agents existing in the environment cooperate as they see fit during run time. We explain the development and communication tools provided in the environment as well as examples of agent realizations and cooperation scenarios. We also show how the multiagent structure is more effective than having a single nonlinear optimization algorithm with randomly selected initial points

Solving Global Optimization Problems Using MANGO

Traditional approaches for solving global optimization problems generally rely on a single algorithm. The algorithm may be hybrid or applied in parallel. Contrary to traditional approaches, this paper proposes to form teams of algorithms to tackle global optimization problems. Each algorithm is embodied and ran by a software agent. Agents exist in a multiagent system and communicate over Our proposed MultiAgent ENvironment for Global Optimization (MANGO). Through Communication and cooperation, the agents complement each other in tasks that they cannot do on their own. This paper gives a formal description of MANGO and Outlines design principles for developing agents to execute Oil MANGO. Our case study shows the effectiveness of multiagent teams in solving global optimization problems

Process synthesis prospective

Abstract Chemical process synthesis methods and tools developed over the last several decades have reached a level of maturity that have provided advantage to practitioners in an environment of increased costs and shrinking margins. Future growth within the chemical process industries is likely to involve even keener competition with greater impact from factors such as raw material and energy availability, climate change mitigation, sustainability, and inherent security. The future will probably see an expanded role for the systematic generation process synthesis paradigm, including an increased interdependency with process and catalytic chemistry on one hand and operability and control expertise on the other. Advances from artificial intelligence may inspire new process synthesis paradigms incorporating more effective representations of the underlying physical sciences and engineering art, new social concerns, new design strategies, and new computerized implementations. The future may also see a collaboration of the systematic generation and superstructure optimization process synthesis paradigms in which systematic generation is used to create the superstructure for simultaneous discrete and continuous variable optimization. As the resulting process designs will certainly be evaluated from additional points of view including social considerations, superstructure optimization will need to produce families of good designs for multi-criteria Pareto optimization. There are many challenges, but continued progress will be made and these challenges will be met