A Smart Initialization on the Swarm Intelligence Based Method for Efficient Search of Optimal Minimum Energy Design

Hsu, Tun-Chieh; Phoa, Frederick Kin Hing

doi:10.1007/978-3-319-93815-8_9

Cited by 6 publications

(4 citation statements)

References 15 publications

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“…It was used for metaheuristics initialization in de Lima Junior et al (2007) and Cai et al (2019). In addition, Hsu and Phoa (2018) came up with an initialization idea, which they called swarm initialization, for a swarm intelligence method they developed earlier. Their idea is to use a Markov chain Monte Carlo (MCMC) pool, and the authors claimed that it can accelerate the convergence of the algorithm.…”

Section: Markov Modelsmentioning

confidence: 99%

Initialization of metaheuristics: comprehensive review, critical analysis, and research directions

Sarhani

Voß

Jovanović

2022

Int Trans Operational Res

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Initialization of metaheuristics is a crucial topic that lacks a comprehensive and systematic review of the state of the art. Providing such a review requires in‐depth study and knowledge of the advances and challenges in the broader field of metaheuristics, especially with regard to diversification strategies, in order to assess the proposed methods and provide insights for initialization. Motivated by the aforementioned research gap, we provide a related review and begin by describing the main metaheuristic methods and their diversification mechanisms. Then, we review and analyze the existing initialization approaches while proposing a new categorization of them. Next, we focus on challenging optimization problems, namely constrained and discrete optimization. Lastly, we give insights on the initialization of local search approaches.

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Section: Markov Modelsmentioning

confidence: 99%

Initialization of metaheuristics: comprehensive review, critical analysis, and research directions

Sarhani

Voß

Jovanović

2022

Int Trans Operational Res

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“…Instead of the computer search in [ 13 ], a recent work by [ 12 ] applied the SIB 1.0 method to obtain an improved result on the search of MEDs, and the search efficiency is also improved. Their results are under an assumption that the particle size is given, and they argued that it was normal because the experimental budget was usually fixed in prior.…”

Section: Demonstration: a Search Of Minimum Energy Designsmentioning

confidence: 99%

“…In addition, SIB also performed well in optimization problems for continuous domains, like efficient construction of confidence sets and the confidence bands for target localization [ 11 ]. Besides, the computational efficiency of the SIB method can be improved via a smart initialization procedure [ 12 ]. Recently, [ 14 ] modified the standard framework of the SIB method by allowing the particle size to be changed during the search.…”

Section: Introductionmentioning

confidence: 99%

A Two-Step Approach to the Search of Minimum Energy Designs via Swarm Intelligence

Phoa

Tsai

2020

Lecture Notes in Computer Science

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Recently, Swarm Intelligence Based (SIB) method, a nature-inspired metaheuristic optimization method, has been widely used in many problems that their solutions fall in discrete and continuous domains. SIB 1.0 is efficient to converge to optimal solution but its particle size is fixed and pre-defined, while SIB 2.0 allows particle size changes during the procedure but it takes longer time to converge. This paper introduces a two-step SIB method that combines the advantages of two SIB methods. The first step via SIB 2.0 serves as a preliminary study to determine the optimal particle size and the second step via SIB 1.0 serves as a follow-up study to obtain the optimal solution. This method is applied to the search of optimal minimum energy design and the result outperforms the results from both SIB 1.0 and SIB 2.0.

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“…With well-defined physical meanings, it efficiently tackles high-dimensional optimization problems in continuous solution spaces, but it may not be the first choice for non-continuous solution spaces that commonly appear in mathematics, statistics, and many other fields, even via the remedy of a simple round-off (Kim et al, 2010). As a result, the SIB method (Phoa, 2017) was proposed for this manner with a wide range of applications, including the constructions of optimal experimental designs , the uniform distribution of testing points (Phoa and Chang, 2016;Huang and Phoa, 2023), supercomputing scheduling (Lin and Phoa, 2019), hot spot determination (Hsu and Phoa, 2018), traveling salesman problem (Yen and Phoa, 2021), and many others.…”

Section: Introductionmentioning

confidence: 99%

An efficient swarm intelligence approach to the optimization on high-dimensional solutions with cross-dimensional constraints, with applications in supply chain management

Liu,

Phoa,

Chen-Burger

et al. 2024

Front. Comput. Neurosci.

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IntroductionThe Swarm Intelligence Based (SIB) method has widely been applied to efficient optimization in many fields with discrete solution domains. E-commerce raises the importance of designing suitable selling strategies, including channel- and direct sales, and the mix of them, but researchers in this field seldom employ advanced metaheuristic techniques in their optimization problem due to the complexities caused by the high-dimensional problems and cross-dimensional constraints.MethodIn this work, we introduce an extension of the SIB method that can simultaneously tackle these two challenges. To pursue faster computing, CPU parallelization techniques are employed for algorithm acceleration.ResultsThe performance of the SIB method is examined on the problems of designing selling schemes in different scales. It outperforms the Genetic Algorithm (GA) in terms of both the speed of convergence and the optimized capacity as measured using improvement multipliers.

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A Smart Initialization on the Swarm Intelligence Based Method for Efficient Search of Optimal Minimum Energy Design

Cited by 6 publications

References 15 publications

Initialization of metaheuristics: comprehensive review, critical analysis, and research directions

Initialization of metaheuristics: comprehensive review, critical analysis, and research directions

A Two-Step Approach to the Search of Minimum Energy Designs via Swarm Intelligence

An efficient swarm intelligence approach to the optimization on high-dimensional solutions with cross-dimensional constraints, with applications in supply chain management

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