A systematic review of the emerging metaheuristic algorithms on solving complex optimization problems

Turgut, Oğuz Emrah; Turgut, Mert Sinan; Kırtepe, Erhan

doi:10.1007/s00521-023-08481-5

Cited by 11 publications

(2 citation statements)

References 114 publications

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“…In essence, metaheuristic algorithms are probabilistic search techniques that share heuristic information to lead the search. Despite the numerous benefits [12], metaheuristic algorithms also have some limitations and barriers [13]. Recently, hybrid metaheuristic optimization is a computational method that combines multiple metaheuristics to solve complex optimization problems [14].…”

Section: Introductionmentioning

confidence: 99%

Accelerated Driving-Training-Based Optimization for Solving Constrained Bi-Objective Stochastic Optimization Problems

Horng,

Lin

2024

Mathematics

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The constrained bi-objective stochastic optimization problem (CBSOP) considers the optimization problem with stochastic bi-objective functions subject to deterministic constraints. The CBSOP is part of a set of hard combinatorial optimization problems regarding time complexity. Ordinal optimization (OO) theory provides a commonly recognized structure to handle hard combinatorial optimization problems. Although OO theory may solve hard combinatorial optimization problems quickly, the deterministic constraints will critically influence computing performance. This work presents a metaheuristic approach that combines driving-training-based optimization (DTBO) with ordinal optimization (OO), abbreviated as DTOO, to solve the CBSOP with a large design space. The DTOO approach comprises three major components: the surrogate model, diversification, and intensification. In the surrogate model, the regularized minimal-energy tensor product with cubic Hermite splines is utilized as a fitness estimation of design. In diversification, an accelerated driving-training-based optimization is presented to determine N remarkable designs from the design space. In intensification, a reinforced optimal computing budget allocation is used to find an extraordinary design from the N remarkable designs. The DTOO approach is applied to a medical resource allocation problem in the emergency department. Simulation results obtained by the DTOO approach are compared with three heuristic approaches to examine the performance of the DTOO approach. Test results show that the DTOO approach obtains an extraordinary design with higher solution quality and computational efficiency than the three heuristic approaches.

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Section: Introductionmentioning

confidence: 99%

Accelerated Driving-Training-Based Optimization for Solving Constrained Bi-Objective Stochastic Optimization Problems

Horng,

Lin

2024

Mathematics

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“…Metaheuristic optimization techniques, known for their minimal derivation, flexibility, and their ability to escape local optima [16], are effective for nonlinear problems, finding applications in energy, mechanical, and chemical engineering [17][18][19]. And, because the optimization process of metaheuristic algorithms does not depend on gradient information [20], they find widespread applications in optimization problems for finding the best parameters. For example, the Dujiangyan irrigation system optimization (DISO) [21] is used to construct a DISO-SVM model [21] to detect the impact of dam displacement on dam operation; Particle Swarm Optimization (PSO) [22] is used to construct PSO-NN [14] and PSO-RF [23] models to predict hydrochar properties; the Grey Wolf Optimizer (GWO) [24] is used to construct a GWO-ELM model [25] for monitoring power quality; the Dandelion Optimizer (DO) [26] is used to improve the efficiency of multilevel inverters [27]; the Jellyfish Search Algorithm (JS) [28] is used to discover unknown parameters in fuel cells [29]; Young's Double-Slit Experiment (YDSE) optimizer [30] is used to construct a YDSE-PWM model for predicting dissolved oxygen levels [31]; the Starling Murmuration Optimizer (SMO) [32] algorithm is used to construct an ADA-SMO model [33] for predicting the strength of the mechanical properties of concrete.…”

Section: Introductionmentioning

confidence: 99%

Improved Dujiangyan Irrigation System Optimization (IDISO): A Novel Metaheuristic Algorithm for Hydrochar Characteristics

Shi,

Zhang,

Sui

et al. 2024

Processes

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Hyperparameter tuning is crucial in the development of machine learning models. This study introduces the nonlinear shrinking factor and the Cauchy mutation mechanism to improve the Dujiangyan Irrigation System Optimization (DISO), proposing the improved Dujiangyan Irrigation System Optimization algorithm (IDISO) for hyperparameter tuning in machine learning. The optimization capabilities and convergence performance of IDISO were validated on 87 CEC2017 benchmark functions of varying dimensions and nine real-world engineering problems, demonstrating that it significantly outperforms DISO in terms of convergence speed and accuracy, and ranks first in overall performance among the seventeen advanced metaheuristic algorithms being compared. To construct a robust and generalizable prediction model for hydrochar element characteristics, this study utilized IDISO and DISO algorithms to fine-tune the parameters of the XGBoost model. The experimental results show that the IDISO-XGBoost model achieved an average prediction performance of 0.95, which represents a 4% improvement over the DISO-XGBoost model. These results indicate that the IDISO algorithm has significant potential and value in practical applications.

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Interpretability, Adaptability and Scalability of Variable Neighborhood Search

Hansen,

Riordan,

Hansen

2024

Lecture Notes in Computer Science

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A systematic review of the emerging metaheuristic algorithms on solving complex optimization problems

Cited by 11 publications

References 114 publications

Accelerated Driving-Training-Based Optimization for Solving Constrained Bi-Objective Stochastic Optimization Problems

Accelerated Driving-Training-Based Optimization for Solving Constrained Bi-Objective Stochastic Optimization Problems

Improved Dujiangyan Irrigation System Optimization (IDISO): A Novel Metaheuristic Algorithm for Hydrochar Characteristics

Interpretability, Adaptability and Scalability of Variable Neighborhood Search

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