Abstract
It is important to know the properties of an optimization problem and the difficulty an algorithm faces to solve it. Population evolvability obtains information related to both elements by analysing the probability of an algorithm to improve current solutions and the degree of those improvements. DPEM_HH is a dynamic multi-objective hyper-heuristic that uses low-level heuristic (LLH) selection methods that apply population evolvability. DPEM_HH uses dynamic multiobjective evolutionary algorithms (DMOEAs) as LLHs to solve dynamic multi-objective optimization problems (DMOPs). Population evolvability is incorporated in DPEM_HH by calculating it on each candidate DMOEA for a set of sampled generations after a change is detected, using those values to select which LLH will be applied. DPEM_HH is tested on multiple DMOPs with dynamic properties that provide several challenges. Experimental results show that DPEM_HH with a greedy LLH selection method that uses average population evolvability values can produce solutions closer to the Pareto optimal front with equal to or better diversity than previously proposed heuristics. This shows the effectiveness and feasibility of the application of population evolvability on hyperheuristics to solve dynamic optimization problems.
Highlights
Optimization techniques have become a significant area concerning industrial, economics, business, and financial systems
While there are many dynamic multiobjective evolutionary algorithms (DMOEAs) proposed in the literature, this paper focuses on the use of DNSGA–II as an element of Dynamic Population–Evolvability based Multi-objective Hyper-heuristic (DPEM_HH) because despite being shown to be outperformed by other recent DMOEAs, it holds a high recognition in the dynamic and non-dynamic optimization area for of its simplicity and effectiveness
The two versions of DPEM_HH are compared by evaluating the offline mean and standard deviation of Ratio of non-dominated individuals (RNI), Inverted generational distance (IGD), Maximum spread (MS) and Hyper–volume ratio (HVR) of the solution set obtained by each algorithm
Summary
Optimization techniques have become a significant area concerning industrial, economics, business, and financial systems. This paper proposes a hyper-heuristic that uses DMOEAs as low-level heuristics to solve DMOPs, named Dynamic Population–Evolvability based Multi-objective Hyper-heuristic (DPEM_HH). In this case, a set of DNSGA–II variations, which will be explained in later sections, is used. The main contributions of this paper can be summarized in three elements: i) The application of population evolvability, a fitness landscape analysis method, as an LLH selection method; ii) A hyper-heuristic capable of solving DMOPs and iii) The use of DMOEAs as LLHs. The remaining sections of this paper follow the order. We explain our conclusions of this paper and the possible future work
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