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Distance-based immune generalised differential evolution algorithm for dynamic multi-objective optimisation

Abstract:

This paper presents distance-based immune generalised differential evolution (DIGDE), an improved algorithmic approach to tackle dynamic multi-objective optimisation problems (DMOPs). Its novelty is using the inverted generational distance (IGD) as an indicator in its selection mechanism to guide the search. DIGDE is based on the immune generalised differential evolution (Immune GDE3) algorithm which combines differential evolution (DE) fast convergence ability and artificial immune systems (AIS) principles for good diversity preservation. A thorough empirical evaluation is carried out on novel benchmark problems configured with different dynamic characteristics. DIGDE's experimental results show an overall improved statistically supported performance in terms of solutions approximation and better achieved distributions. Using IGD as a searching indicator allows DIGDE to achieve better performance and robustness in comparison to state-of-the-art methods when facing different change frequencies and severity levels.