A Multi-agent System for Hybrid Optimization

TL;DR

This paper introduces a multi-agent system that combines various optimization solvers, including direct search and meta-heuristics, to efficiently solve complex, black-box optimization problems in process engineering through cooperation and competition.

Contribution

It presents a novel multi-agent framework enabling simultaneous application and coordination of diverse optimization methods for complex problems.

Findings

Enhanced optimization performance through solver cooperation.

Demonstrated effectiveness on process engineering case studies.

Potential for improved resource utilization in optimization tasks.

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.