Novel Prediction Strategies for Dynamic Multiobjective Optimization

A Novel Prediction Strategy Based on Change Degree of Decision Variables for Dynamic Multi-Objective Optimization

“…The definition of DMOPs is a standard formula proposed by Farina et al [3] and the formula is used in most literature [1]. The time instance, t, is a discrete time defined [9] as…”

“…Many DMOEAs have been proposed in recent years for dealing with DMOPs well, and most existing algorithms can be classified into the different categories (i.e., memory approaches [37], [38], prediction approaches [7], [9], [22], [31], diversity approaches [32], [33], multipopulation approaches [20], [28] and hybrid strategy [34]), not restricted to evolutionary DMO. This section mainly focuses on the prediction approaches.…”

“…Consequently, most of literatures have been reported according to this idea to exploit the predictability of dynamic environments. For example, Zhang et al put forward novel prediction strategies [9], which combines a new prediction-based reaction mechanism and a popular regularity model-based multi-objective estimation of distribution algorithm; Rong et al proposed a multi-model [40]; and so on. These kind of algorithms have solved the DMOPs to some extend, but their effectiveness is still questionable if the change in the environment is, as DMOPs with different the challenge characteristic lead to mostly happens or hardly predictable.…”

“…The dynamic optimization merely happens after detecting an environmental change [9] in the whole optimization process. Therefore, how to successfully promote the competition and cooperation between the forces of multi-objective optimization and dynamic optimization is a key design point for EAs with change response.…”

“…Dynamic multi-objective optimization problems (DMOPs) [1]- [4] not only have multiple conflicting objectives, but also need to deal with the tradeoffs and the time-dependent objective functions, constraints and/or parameters. Although static multi-objective optimization evolutionary algorithms (MOEAs) [5], [6] have the superior capability on addressing the static multi-objective optimization problems, it still is a challenging task as static MOEAs lack the capability of dynamic optimization [7]- [9] on dealing with DMOPs. Nevertheless, there has been an increasing amount of research interest in the field of dynamic multi-objective optimization (DMO) for better solving DMOPs since many real-world applications, like dynamic scheduling [10]- [13], path planning [14]- [16], resource allocation [17], [18], and machine learning [19].…”