A Multi-agent System for Hybrid Optimization (2501.09563v1)

Abstract: Optimization problems in process engineering, including design and operation, can often pose challenges to many solvers: multi-modal, non-smooth, and discontinuous models often with large computational requirements. In such cases, the optimization problem is often treated as a black box in which only the value of the objective function is required, sometimes with some indication of the measure of the violation of the constraints. Such problems have traditionally been tackled through the use of direct search and meta-heuristic methods. The challenge, then, is to determine which of these methods or combination of methods should be considered to make most effective use of finite computational resources. This paper presents a multi-agent system for optimization which enables a set of solvers to be applied simultaneously to an optimization problem, including different instantiations of any solver. The evaluation of the optimization problem model is controlled by a scheduler agent which facilitates cooperation and competition between optimization methods. The architecture and implementation of the agent system is described in detail, including the solver, model evaluation, and scheduler agents. A suite of direct search and meta-heuristic methods has been developed for use with this system. Case studies from process systems engineering applications are presented and the results show the potential benefits of automated cooperation between different optimization solvers and motivates the implementation of competition between solvers.

• The paper introduces a multi-agent architecture that deploys diverse optimization solvers concurrently to enhance process engineering designs.
• It uses a proxy function and scheduler agent to balance global exploration with local exploitation through coordinated solver interactions.
• Case studies on heat exchanger design and radionuclide detection show improved solution quality and reduced computational time compared to traditional methods.

A Multi-Agent System for Hybrid Optimization in Process Engineering

The discussed paper presents an innovative approach to addressing complex optimization problems in process engineering via a multi-agent system that enables simultaneous application of various solvers. Process engineering optimizations often involve design and operational challenges characterized by multi-modal, non-smooth, and discontinuous models that are computationally demanding. Traditional approaches utilizing direct search and meta-heuristic methods are re-evaluated through this proposed multi-agent architecture, offering cooperative and competitive interactions among solvers to optimize problem-solving efficacy.

Overview and System Architecture

The crux of this research lies in its multi-agent system capable of deploying a suite of solvers concurrently on optimization problems. The paper outlines the system's three primary agents—the solver, model evaluation, and scheduler agents. The solver agents symbolize individual optimization methods, each possibly configured with different parameter settings, allowing dynamic exploration and exploitation of optimization domains. Contrary to directly passing the objective function to solvers, a proxy function is used to interact with these agents, further managed by a scheduler agent that coordinates task distribution according to priority-based queues.

Case Studies and Results

Two case studies from process systems engineering are highlighted to demonstrate the system's efficacy: a heat exchanger network design problem and the design of a micro-analytic detection system for radionuclides. Importantly, these studies emphasize the system's ability to harness solver cooperation, yielding improved outcomes and reduced computational times compared to independent solver operations.

In the first case paper, cooperative solver behavior led to better solutions much faster by balancing global exploration via meta-heuristic methods with local exploitation strengths of direct search methods—indicative of effective solver hybridization. The second case paper showcased the system's proficiency in computationally expensive multi-objective design problems. The cooperative sharing of solutions thrust the multi-agent setup ahead of independent methods, fostering consistent, superior non-dominated solution sets using rigorous quality measures like hypervolume and generational distance.

Implications and Future Directions

The proposed system provides a novel paradigm for handling optimization problems with characteristics that challenge traditional mathematical programming approaches, such as discontinuities and nonlinearities. By merging different optimization strategies dynamically, the multi-agent system surpasses limitations of individual methods, effectively optimizing complex, real-world engineering problems.

Moreover, the paper suggests avenues for future research, including more sophisticated scheduling algorithms to optimize solver coordination further and introducing competitive elements to enhance resource allocation efficiently. The anticipated evolution of this system could significantly impact various sectors within AI and process engineering, propelling the management of computational resources in line with solution-seeking priorities amidst uncertain and complex design landscapes.

In conclusion, this multi-agent system lays a foundational framework for hybrid optimization, leveraging collective solver intelligence to navigate the intricacies of process engineering challenges. As such, it marks an essential step toward more adaptive and intelligent optimization tools within the field.