The Construction of Dynamic Multi-objective Optimization Test Functions

Tang, Min; Huang, Zhijian; Chen, Guangxi

doi:10.1007/978-3-540-74581-5_8

Cited by 5 publications

(2 citation statements)

References 4 publications

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“…For example, a Type II DMOOP can be constructed from DTF by setting the following parameter values: n = 20, α(t) = 0.2 + 4.8t 2 , β(t) = 10 2 sin(0.5πt) , γ (t) = sin(0.5π t), ψ(t) = ts with s ∈ R, and ω(t) ∝ ψ(t). Tang et al [2007] also suggested constructing DMOOPs based on the ZDT functions of Deb [1999]. Three objective functions are constructed similar to the DMOOPs of Farina et al [2004] and provide an additional explanation of how the POF is calculated.…”

Section: Dmoo Benchmark Functions Currently Usedmentioning

confidence: 99%

Benchmarks for dynamic multi-objective optimisation algorithms

Helbig

Engelbrecht

2014

ACM Comput. Surv.

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Algorithms that solve Dynamic Multi-Objective Optimisation Problems (DMOOPs) should be tested on benchmark functions to determine whether the algorithm can overcome specific difficulties that can occur in real-world problems. However, for Dynamic Multi-Objective Optimisation (DMOO), no standard benchmark functions are used. A number of DMOOPs have been proposed in recent years. However, no comprehensive overview of DMOOPs exist in the literature. Therefore, choosing which benchmark functions to use is not a trivial task. This article seeks to address this gap in the DMOO literature by providing a comprehensive overview of proposed DMOOPs, and proposing characteristics that an ideal DMOO benchmark function suite should exhibit. In addition, DMOOPs are proposed for each characteristic. Shortcomings of current DMOOPs that do not address certain characteristics of an ideal benchmark suite are highlighted. These identified shortcomings are addressed by proposing new DMOO benchmark functions with complicated Pareto-Optimal Sets (POSs), and approaches to develop DMOOPs with either an isolated or deceptive Pareto-Optimal Front (POF). In addition, DMOO application areas and real-world DMOOPs are discussed.

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Section: Dmoo Benchmark Functions Currently Usedmentioning

confidence: 99%

Benchmarks for dynamic multi-objective optimisation algorithms

Helbig

Engelbrecht

2014

ACM Comput. Surv.

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“…Some of the principal benchmark dynamic multi-objective optimization problems (DMOPs) were defined nearly two decades ago by Farina et al [12]. Since then, a plethora of benchmark problems have been proposed [8,13,17,19,20,22,32,33,[36][37][38], allowing for the testing of different characteristics of real world systems in a controllable environment. Dynamic Optimization Problem (DOP) generators such as Moving Peaks [4,36] and the Dynamic XOR [35] and others are well-known single-objective environments in which the difficulty of a problem instance can be controlled by increasing the number of peaks or bits respectively.…”

Section: Introductionmentioning

confidence: 99%

Reproducibility and Baseline Reporting for Dynamic Multi-objective Benchmark Problems

Herring¹,

Kirley²,

Yao³

2022

Preprint

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Dynamic multi-objective optimization problems (DMOPs) are widely accepted to be more challenging than stationary problems due to the time-dependent nature of the objective functions and/or constraints. Evaluation of purpose-built algorithms for DMOPs is often performed on narrow selections of dynamic instances with differing change magnitude and frequency or a limited selection of problems. In this paper, we focus on the reproducibility of simulation experiments for parameters of DMOPs. Our framework is based on an extension of PlatEMO, allowing for the reproduction of results and performance measurements across a range of dynamic settings and problems. A baseline schema for dynamic algorithm evaluation is introduced, which provides a mechanism to interrogate performance and optimization behaviours of well-known evolutionary algorithms that were not designed specifically for DMOPs. Importantly, by determining the maximum capability of non-dynamic multi-objective evolutionary algorithms, we can establish the minimum capability required of purpose-built dynamic algorithms to be useful. The simplest modifications to manage dynamic changes introduce diversity. Allowing non-dynamic algorithms to incorporate mutated/random solutions after change events determines the improvement possible with minor algorithm modifications. Future expansion to include current dynamic algorithms will enable reproduction of their results and verification of their abilities and performance across DMOP benchmark space.

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