PopDMMO: A general framework of population-based stochastic search algorithms for dynamic multimodal optimization

Lin, Xin; Luo, Wenjian; Xu, Peilan; Qiao, Yingying; Yang, Shengxiang

doi:10.1016/j.swevo.2021.101011

Cited by 10 publications

(3 citation statements)

References 44 publications

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“…More recently, some authors proposed the Deterministic Distortion and Rotation Benchmark (DDRB) [339], a method to generate Deterministic Dynamic Multimodal Optimisation Problems (DMMOP) considering both dynamic and multimodal characteristics, which can simulate more diverse sets of challenges. In the same context, another set of benchmark problems, as well as an optimisation framework, called PopDMMO, containing several population-based algorithms, was designed in [340]). To address the need to continuously adapt to landscape changes, some improvements in cGA have been proposed in [341], based on techniques of hypermutation and random immigrants.…”

Section: Future Research Directionsmentioning

confidence: 99%

Metaheuristics in the Balance: A Survey on Memory-Saving Approaches for Platforms with Seriously Limited Resources

Khalfi,

Caraffini,

Iacca

2023

International Journal of Intelligent Systems

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In the last three decades, the field of computational intelligence has seen a profusion of population-based metaheuristics applied to a variety of problems, where they achieved state-of-the-art results. This remarkable growth has been fuelled and, to some extent, exacerbated by various sources of inspiration and working philosophies, which have been thoroughly reviewed in several recent survey papers. However, the present survey addresses an important gap in the literature. Here, we reflect on a systematic categorisation of what we call “lightweight” metaheuristics, i.e., optimisation algorithms characterised by purposely limited memory and computational requirements. We focus mainly on two classes of lightweight algorithms: single-solution metaheuristics and “compact” optimisation algorithms. Our analysis is mostly focused on single-objective continuous optimisation. We provide an updated and unified view of the most important achievements in the field of lightweight metaheuristics, background concepts, and most important applications. We then discuss the implications of these algorithms and the main open questions and suggest future research directions.

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Section: Future Research Directionsmentioning

confidence: 99%

Metaheuristics in the Balance: A Survey on Memory-Saving Approaches for Platforms with Seriously Limited Resources

Khalfi,

Caraffini,

Iacca

2023

International Journal of Intelligent Systems

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“…Among the various possible ROOT M problems, only the multi-objective ROOT M Q has been studied in the existing literature [15][16][17][18], where the set of deployed solutions consists of Pareto-optimal solutions (POS). However, it is important to note that ROOT M is not exclusively limited to multi-objective problems, and there are other classes of ROOT M problems, such as multimodal ones [19], where the search space contains multiple moving global optima [20,21].…”

mentioning

confidence: 99%

Robust Optimization Over Time: A Critical Review

Yazdani,

Omidvar,

Yazdani

et al. 2024

IEEE Trans. Evol. Computat.

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Robust optimization over time (ROOT) is the combination of robust optimization and dynamic optimization. In ROOT, frequent changes to deployed solutions are undesirable, which can be due to the high cost of switching between deployed solutions, limitations on the resources required to deploy new solutions, and/or the system's inability to tolerate frequent changes in the deployed solutions. ROOT is dedicated to the study and development of algorithms capable of dealing with the implications of deploying or maintaining solutions over longer time horizons involving multiple environmental changes. This paper presents an in-depth review of the research on ROOT. The overarching aim of this survey is to help researchers gain a broad perspective on the current state of the field, what has been achieved so far, and the existing challenges and pitfalls. This survey also aims to improve accessibility and clarity by standardizing terminology and unifying mathematical notions used across the field, providing explicit mathematical formulations of definitions, and improving many existing mathematical descriptions. Moreover, we classify ROOT problems based on two ROOT-specific criteria: the requirements for changing or keeping deployed solutions and the number of deployed solutions. This classification helps researchers gain a better understanding of the characteristics and requirements of ROOT problems, which is crucial to systematic algorithm design and benchmarking. Additionally, we classify ROOT methods based on the approach they use for finding robust solutions and provide a comprehensive

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“…In the aforementioned niching techniques, the entire population evolves together and genetic operators are designed to preserve the population diversity. In contrast, some niching algorithms divide the entire population into parallel subpopulations, including forking GA [38], multinational GA [39], multipopulation GA [40], NichePSO [41], speciation-based PSO (SPSO) [42], swarms [43,44], culture algorithm with fuzzing cluster [45], LAM-ACO [46], and dual-strategy DE (DSDE) [47]. Each subpopulation evolves independently, searching for its own optimum.…”

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confidence: 99%

A Partition-Based Random Search Method for Multimodal Optimization

Lin

Matta

et al. 2022

Mathematics

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Practical optimization problems are often too complex to be formulated exactly. Knowing multiple good alternatives can help decision-makers easily switch solutions when needed, such as when faced with unforeseen constraints. A multimodal optimization task aims to find multiple global optima as well as high-quality local optima of an optimization problem. Evolutionary algorithms with niching techniques are commonly used for such problems, where a rough estimate of the optima number is required to determine the population size. In this paper, a partition-based random search method is proposed, in which the entire feasible domain is partitioned into smaller and smaller subregions iteratively. Promising regions are partitioned faster than unpromising regions, thus, promising areas will be exploited earlier than unpromising areas. All promising areas are exploited in parallel, which allows multiple good solutions to be found in a single run. The proposed method does not require prior knowledge about the optima number and it is not sensitive to the distance parameter. By cooperating with local search to refine the obtained solutions, the proposed method demonstrates good performance in many benchmark functions with multiple global optima. In addition, in problems with numerous local optima, high-quality local optima are captured earlier than low-quality local optima.

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PopDMMO: A general framework of population-based stochastic search algorithms for dynamic multimodal optimization

Cited by 10 publications

References 44 publications

Metaheuristics in the Balance: A Survey on Memory-Saving Approaches for Platforms with Seriously Limited Resources

Metaheuristics in the Balance: A Survey on Memory-Saving Approaches for Platforms with Seriously Limited Resources

Robust Optimization Over Time: A Critical Review

A Partition-Based Random Search Method for Multimodal Optimization

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