A novel greedy genetic algorithm-based personalized travel recommendation system

Paulavičius, Remigijus; Stripinis, Linas; Sutavičiutė, Simona; Kočegarov, Dmitrij; Filatovas, Ernestas

doi:10.1016/j.eswa.2023.120580

Cited by 16 publications

(4 citation statements)

References 62 publications

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“…Derivative-Free Global Optimization (DFGO) problems that require significant computational resources can be found in a wide range of fields, including robotics (Hauser, 2017;Wang et al, 2020), engineering design (Lin et al, 2022;Kim et al, 2022b), economics (Liu et al, 2022;Kim et al, 2022a), tourism (Liao et al, 2021;Paulavičius et al, 2023), and many others (Floudas et al, 2013;Moret et al, 2016;Grigaitis et al, 2007;. These problems often involve black-box functions that require expensive simulations or experiments for evaluation.…”

Section: Introductionmentioning

confidence: 99%

Review and Computational Study on Practicality of Derivative-Free DIRECT-Type Methods

Stripinis,

Paulavičius

2024

Informatica

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Derivative-free DIRECT-type global optimization algorithms are increasingly favoured for their simplicity and effectiveness in addressing real-world optimization challenges. This review examines their practical applications through a systematic analysis of scientific journals and computational studies. In particular, significant challenges in reproducibility have been identified with practical problems. To address this, we conducted an experimental study using practical problems from reputable CEC libraries, comparing DIRECT-type techniques against their state-of-the-art counterparts. Therefore, this study sheds light on current gaps, opportunities, and future prospects for advanced research in this domain, laying the foundation for replicating and expanding the research findings presented herein.

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Section: Introductionmentioning

confidence: 99%

Review and Computational Study on Practicality of Derivative-Free DIRECT-Type Methods

Stripinis,

Paulavičius

2024

Informatica

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“…This enhances decision-making quality, shortens decision-making time, and reduces decision-making costs [26]. Decision support methods are, thus, widely applied in various fields to establish an efficient expert system to solve optimization problems such as product design [27][28][29], recommendation systems [30][31][32], and system improvement [33][34][35]. Below, we introduce the two most common approaches to decision support systems: numerical optimization and heuristic algorithms.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Decision Support for Irrigation Systems Based on Skyline Query

Loh,

Chen,

et al. 2024

Applied Sciences

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The steady increase in droughts worldwide has compelled many researchers to focus on water allocation. Multi-objective decision support for irrigation systems is a popular topic due to its relevance to the national economy and food supply. However, the majority of researchers have relied on conventional top-k designs for their decision support systems despite their limitations with regard to multi-objective systems. Thus, we propose applying a skyline query to the problem. As the input and output formats of skyline queries differ significantly from those of existing systems, we developed a new genetic algorithm and objective ranking. Qualitative and quantitative experiments using real-world data from Taiwan’s largest irrigated region demonstrate the effectiveness of the proposed approach.

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“…Examples of such algorithms include genetic [4], particle swarm [5], and simulated annealing [6], which have achieved significant popularity and adoption. However, there have been developments in the form of more efficient methods and extensions [7][8][9]. Furthermore, advanced optimization methods such as Bayesian optimization [10] and the radial basis function [11] employ surrogate models to approximate the objective function, enabling an efficient guide of the search process.…”

Section: Introductionmentioning

confidence: 99%

Novel Algorithm for Linearly Constrained Derivative Free Global Optimization of Lipschitz Functions

Stripinis

Paulavičius

2023

Mathematics

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This paper introduces an innovative extension of the DIRECT algorithm specifically designed to solve global optimization problems that involve Lipschitz continuous functions subject to linear constraints. Our approach builds upon recent advancements in DIRECT-type algorithms, incorporating novel techniques for partitioning and selecting potential optimal hyper-rectangles. A key contribution lies in applying a new mapping technique to eliminate the infeasible region efficiently. This allows calculations to be performed only within the feasible region defined by linear constraints. We perform extensive tests using a diverse set of benchmark problems to evaluate the effectiveness and performance of the proposed algorithm compared to existing DIRECT solvers. Statistical analyses using Friedman and Wilcoxon tests demonstrate the superiority of a new algorithm in solving such problems.

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A novel greedy genetic algorithm-based personalized travel recommendation system

Cited by 16 publications

References 62 publications

Review and Computational Study on Practicality of Derivative-Free DIRECT-Type Methods

Review and Computational Study on Practicality of Derivative-Free DIRECT-Type Methods

Multi-Objective Decision Support for Irrigation Systems Based on Skyline Query

Novel Algorithm for Linearly Constrained Derivative Free Global Optimization of Lipschitz Functions

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