  We propose PESA, a novel approach combining Particle Swarm Optimisation
(PSO), Evolution Strategy (ES), and Simulated Annealing (SA) in a hybrid
Algorithm, inspired from reinforcement learning. PESA hybridizes the three
algorithms by storing their solutions in a shared replay memory. Next, PESA
applies prioritized replay to redistribute data between the three algorithms in
frequent form based on their fitness and priority values, which significantly
enhances sample diversity and algorithm exploration. Additionally, greedy
replay is used implicitly within SA to improve PESA exploitation close to the
end of evolution. The validation against 12 high-dimensional continuous
benchmark functions shows superior performance by PESA against standalone ES,
PSO, and SA, under similar initial starting points, hyperparameters, and number
of generations. PESA shows much better exploration behaviour, faster
convergence, and ability to find the global optima compared to its standalone
counterparts. Given the promising performance, PESA can offer an efficient
optimisation option, especially after it goes through additional
multiprocessing improvements to handle complex and expensive fitness functions.
