An Agent-based Approach to Decentralized Global Optimization Adapting COHDA to Coordinate Descent

Heuristics like evolution strategies have been successfully applied to optimization problems with rugged, multi-modal fitness landscapes, to non-linear problems, and to derivative free optimization. Parallelization for acceleration often involves domain specific knowledge for data domain partition or functional or algorithmic decomposition. We present an agent-based approach for a fully decentralized global optimization algorithm without specific decomposition needs. The approach extends the ideas of coordinate descent to a gossiping like decentralized agent approach with the advantage of escaping local optima by replacing the line search with a full 1-dimensional optimization and by asynchronously searching different parts of the search space using agents. We compare the new approach with the established covariance matrix adaption evolution strategy and demonstrate the competitiveness of the decentralized approach even compared to a centralized algorithm with full information access. The evaluation is done using a bunch of well-known benchmark functions.