$$\mu $$ μ JADE: adaptive differential evolution with a small population

Abstract

This paper proposes a new differential evolution (DE) algorithm for unconstrained continuous optimisation problems, termed $$\mu $$μJADE, that uses a small or `micro' ($$\mu $$μ) population. The main contribution of the proposed DE is a new mutation operator, `current-by-rand-to-pbest.' With a population size less than 10, $$\mu $$μJADE is able to solve some classical multimodal benchmark problems of 30 and 100 dimensions as reliably as some state-of-the-art DE algorithms using conventionally sized populations. The algorithm also compares favourably to other small population DE variants and classical DE.