Improved Beluga Whale Optimization for Solving the Simulation Optimization Problems with Stochastic Constraints

Abstract

Simulation optimization problems with stochastic constraints are optimization problems with deterministic cost functions subject to stochastic constraints. Solving the considered problem by traditional optimization approaches is time-consuming if the search space is large. In this work, an approach integration of beluga whale optimization and ordinal optimization is presented to resolve the considered problem in a relatively short time frame. The proposed approach is composed of three levels: emulator, diversification, and intensification. Firstly, the polynomial chaos expansion is treated as an emulator to evaluate a design. Secondly, the improved beluga whale optimization is proposed to seek N candidates from the whole search space. Eventually, the advanced optimal computational effort allocation is adopted to determine a superior design from the N candidates. The proposed approach is utilized to seek the optimal number of service providers for minimizing staffing costs while delivering a specific level of care in emergency department healthcare. A practical example of an emergency department with six cases is used to verify the proposed approach. The CPU time consumes less than one minute for six cases, which demonstrates that the proposed approach can meet the requirement of real-time application. In addition, the proposed approach is compared to five heuristic methods. Empirical tests indicate the efficiency and robustness of the proposed approach.

Bridging artificial intelligence and fucoxanthin for the recovery and quantification from microalgae

“…Moving on, swarm intelligence (SI) algorithms such as starling murmuration optimizer (SMO), golden jackal optimization (GJO), white shark optimizer (WSO), dandelion optimizer (DO), search in forest optimizer (SIFO), snake optimizer (SO), and beluga whale optimization (BWO) are broadly applied in various sectors and proven to perform better as compared to conventional optimization methods. Out of these optimizers, BWO is a trending nature inspired system that mimics the attacking and feeding behaviors of beluga whales [ 116 ]. The advantages of BWO can provide higher stability, search ability, convergence rate and speed, but lack in terms of premature convergence and risk of being trapped in local optimum.…”

“…The advantages of BWO can provide higher stability, search ability, convergence rate and speed, but lack in terms of premature convergence and risk of being trapped in local optimum. Recently, Horng and Lin [ 116 ] came out with a improved BWO or IBWO, with improvement in the learning approach, acceleration of searching process, and variety and consistency of chosen candidates in order to provide a much reliable optimization process.…”

Enhanced Whale Optimization Algorithm for Improved Transient Electromagnetic Inversion in the Presence of Induced Polarization Effects

“…By referring to the solutions of other heuristic optimization algorithms to solve engineering problems, and improving the way of evolution of heuristic algorithms, an applicable multi-threading technology can be developed based on the characteristics of the optimizer [31], and the search efficiency can be improved by optimizing the population of candidate solutions [32][33][34]. The binary scheme selector [35], the optimization strategy selector [36][37][38], and the dynamic parameter selector [36,37,39] can be used.…”

Intelligent and sustainable waste classification model based on multi-objective beluga whale optimization and deep learning

“…BWO has proved its efficiency in solving many optimization problems, such as engineering design problems (Jia et al 2023 ), feature selection optimization problems (Gao et al 2023 ), and simulation optimization problems with stochastic constraints (Horng and Lin 2023 ). Additionally, it has shown great performance in finding the optimal hyperparameter values of the VGG deep convolutional neural network as discussed in Deepika and Kuchibhotla’s ( 2024 ) study, the optimal hyperparameter values of the DeepLabv3-based semantic segmentation architecture as discussed in Anilkumar and Venugopal’s ( 2023 ) study, and the optimal hyperparameter values of the convolutional bidirectional long short-term memory with an autoencoder model as discussed in Asiri et al’s ( 2024 ) study.…”