Event-Driven Multi-algorithm Optimization: Mixing Swarm and Evolutionary Strategies

Researchers in nature-inspired optimization have recently proposed multi-population asynchronous algorithms that split the evolutionary process between different search paradigms working in collaboration. These algorithms execute the optimization strategy by reading streams of messages containing solution populations from message queues. After searching for a small number of iterations, new evolved populations are generated and sent back to a queue. Current research suggests that when we have many population-processing algorithms communicating in parallel, parameters intensifying exploration or exploitation in each population strike a dynamic that balances the two, exploring and exploiting simultaneously, maintaining an overall diversity, and improving the search. In this work, we propose a simple reactive migration, population-generation, and processing method for the asynchronous processing of multi-population, multi-strategy algorithms that achieves an improvement over homogeneous configurations. We evaluate this method by comparing a heterogeneous ensemble of multi-populations against a homogeneous solution consisting of a Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) populations, using five problems from the noiseless BBOB toolbox for the optimization of continuous functions. Results show that compared with other asynchronous homogeneous population-based algorithms, this method offers better performance concerning the maximum number of evaluations needed to find a solution and the number runs where it found it.