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Directional mutation and crossover for immature performance of whale algorithm with application to engineering optimization

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2022年第2期9卷 519-563页

作者： Qi, Ailiang Zhao, Dong Yu, Fanhua Heidari, Ali Asghar Chen, Huiling Xiao, Lei Changchun Normal Univ Coll Comp Sci & Technol Changchun 130032 Jilin Peoples R China Natl Univ Singapore Sch Comp Dept Comp Sci Singapore Singapore Wenzhou Univ Coll Comp Sci & Artificial Intelligence Wenzhou 325035 Zhejiang Peoples R China

In recent years, a range of novel and pseudonovel optimization algorithms has been proposed for solving engineering problems. Swarm intelligence optimization algorithms (SIAs) have become popular methods, and the whale optimization algorithm (WOA) is one of the highly discussed SIAs. However, regardless of novelty concerns about this method, the basic WOA is a weak method compared to top differential evolutions and particle swarm variants, and it suffers from the problem of poor initial population quality and slow convergence speed. Accordingly, in this paper, to increase the diversity of WOA versions and enhance the performance of WOA, a new WOA variant, named LXMWOA, is proposed, and based on the Levy initialization strategy, the directional crossover mechanism, and the directional mutation mechanism. Specifically, the introduction of the Levy initialization strategy allows initial populations to be dynamically distributed in the search space and enhances the global search capability of the WOA. Meanwhile, the directional crossover mechanism and the directional mutation mechanism can improve the local exploitation capability of the WOA. To evaluate its performance, using a series of functions and three models of engineering optimization problems, the LXMWOA was compared with a broad array of competitive optimizers. The experimental results demonstrate that the LXMWOA is significantly superior to its exploration and exploitation capability peers. Therefore, the proposed LXMWOA has great potential to be used for solving engineering problems.

关键词： swarm intelligence single-objective optimization whale optimization algorithm metaheuristic engineering design

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optimization of active controlled system for structures using metaheuristic algorithms

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EARTHQUAKES AND STRUCTURES 2024年第5期27卷 401-417页

作者： Mehta, Nirmal S. Bhaiya, Vishisht SVNIT Surat Dept Civil Engn Surat India

This study presents a method for optimization of weighting matrices of the linear quadratic regulator (LQR) control algorithm in order to design an optimal active control system using metaheuristic algorithms. The LQR is a widely used control technique in engineering for designing optimal controllers for linear systems by minimizing a quadratic cost function. However, the performance of the LQR strongly depends on the appropriate selection of weighting matrices, which are usually determined by some thumb rule or exhaustive search method. In the present study, for the optimization of weighting matrices, four metaheuristic algorithms including, Particle Swarm optimization (PSO), Genetic algorithm (GA), Grey Wolf Optimizer (GWO) and, whale optimization algorithm (WOA) are considered. To generate optimal weighting matrices, the objective function used consists of displacement and absolute acceleration. During the optimization process, a response effectiveness factor is also checked for displacement and acceleration as a constraint for the proper selection of weighting matrices. To study the effectiveness of optimized active control system to those for the exhaustive search method, the various controlled responses of the system are compared with the corresponding uncontrolled system. The optimized weighting matrices effectively reduce the displacement, velocity, and acceleration responses of the structure. Based on the simulation study, it can be observed that GWO performs well compared to the PSO, GA, and WO algorithms. By employing metaheuristic algorithms, this study showcases a more efficient and effective approach to finding optimal weighting matrices, thereby enhancing the performance of active control systems.

关键词： active control system genetic algorithm grey wolf optimization linear quadratic regulator metaheuristic algorithms optimization particle swarm optimization whale optimization algorithm

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Research on the Switch Migration Strategy Based on Global optimization

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2024年第7期E107D卷 825-834页

作者： Bao, Xiao'an Zhou, Shifan Wu, Biao Tu, Xiaomei Jin, Yuting Zhang, Qingqi Zhang, Na Zhejiang Sci Tech Univ Hangzhou Peoples R China

With the popularization of software defined networks, switch migration as an important network management strategy has attracted increasing attention. Most existing switch migration strategies only consider local conditions and simple load thresholds, without fully considering the overall optimization and dynamics of the network. Therefore, this article proposes a switch migration algorithm based on global optimization. This algorithm adds a load prediction module to the migration model, determines the migration controller, and uses an improved whale optimization algorithm to determine the target controller and its surrounding controller set. Based on the load status of the controller and the traffic priority of the switch to be migrated, the optimal migration switch set is determined. The experimental results show that compared to existing schemes, the algorithm proposed in this paper improves the average flow processing efficiency by 15% to 40%, reduces switch migration times, and enhances the security of the controller.

关键词： software-defined network whale optimization algorithm load balancing load prediction switch migrations

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Multi-threshold image segmentation using a boosted whale optimization: case study of breast invasive ductal carcinomas

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第10期27卷 14891-14949页

作者： Shi, Jinge Chen, Yi Cai, Zhennao Heidari, Ali Asghar Chen, Huiling He, Qiuxiang Wenzhou Univ Inst Big Data & Informat Technol Wenzhou 325035 Peoples R China Univ Tehran Coll Engn Sch Surveying & Geospatial Engn Tehran Iran Wenzhou Med Univ Affiliated Hosp 1 Dept Pathol Wenzhou Zhejiang Peoples R China

Medical imaging is essential in modern healthcare because it assists physicians in the diagnosis of cancer. Various tissues and features in medical imaging can be recognized using image segmentation algorithms. This feature makes it possible to pinpoint and define particular areas, which makes it easier to precisely locate and characterize anomalities or lesions for cancer diagnosis. Among cancers affecting women, breast cancer is particularly prevalent, underscoring the urgent need to improve the accuracy of image segmentation for breast cancer in order to assist medical practitioners. Multi-threshold image segmentation is widely acknowledged for its direct and effective characteristics. In this context, this paper suggests a refined whale optimization algorithm to improve the segmentation accuracy of breast cancer data. This algorithm optimizes performance by combining a quantum phase interference mechanism and an enhanced solution quality strategy. This work compares the method with classical, homogeneous, state-of-the-art algorithms and runs experiments on the IEEE CEC2017 benchmark to validate its practical optimization performance. Furthermore, a multi-threshold image segmentation algorithm-based image segmentation technique is presented in this study. The Berkeley segmentation dataset and the breast invasive ductal carcinomas segmentation dataset are segmented using the approach using a non-local means two-dimensional histogram and Renyi's entropy. Experimental results demonstrate the excellent performance of this segmentation method in image segmentation applications across both low and high threshold levels. As a result, it emerges as a valuable image segmentation technique with practical applications.

关键词： whale optimization algorithm Quantum phase Image segmentation 2D Renyi's entropy Non-local means 2D histogram

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Multi-objective task scheduling in cloud data centers: a differential evolution chaotic whale optimization approach

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INTERNATIONAL JOURNAL OF INTERACTIVE DESIGN AND MANUFACTURING - IJIDEM 2025年第6期19卷 4417-4427页

作者： Cui, Xiang Guangzhou Polytech Sports Basic Study Dept Guangzhou 510650 Guangdong Peoples R China

Cloud computing has transformed the accessibility of scalable computational resources, emphasizing the need for efficient task scheduling to optimize resource utilization. This study presents DECWOA, a novel Differential Evolution Chaotic whale optimization algorithm designed to enhance scheduling in cloud data centers. DECWOA integrates concepts from Sine chaos theory, employing chaotic initialization processes based on sine functions to promote exploration diversity. Moreover, it incorporates adaptive inertia weights that dynamically adjust exploration and exploitation tendencies, along with differential variance to minimize solution space, thereby improving convergence. The algorithm achieves significant reductions in task and workflow execution durations, demonstrating a remarkable 64% decrease in execution time and an 11% reduction in data center costs. Through comprehensive comparisons with various scheduling algorithms and meta-heuristics using Cloudsim Plus, DECWOA consistently outperforms alternatives such as AIGA and GA. Furthermore, its adaptability to parameter variations ensures superior solutions across diverse configurations. This research underscores the effectiveness of DECWOA in multi-objective task scheduling, highlighting its pivotal role in accelerating convergence rates, cutting operational costs, and enhancing cloud service efficiency.

关键词： Cloud computing Scheduling Resource utilization optimization whale optimization algorithm Chaos theory

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whale optimization-Based Access Control Scheme in D2D Communication Underlaying Cellular Networks

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2024年第3期21卷 3416-3427页

作者： Jadav, Nilesh Kumar Tanwar, Sudeep Nirma Univ Inst Technol Dept Comp Sci & Engn Ahmadabad 382481 India

Integration of device-to-device (D2D) communication has gained significant attention within cellular networks as a means to enhance their capacity, coverage, and performance. Despite these advantages, D2D communication encounters various challenges, such as high interference, resource allocation, energy efficiency, and security. In this paper, we investigate the problem associated with resource allocation in D2D communication underlying cellular networks. The existing resource allocation schemes (e.g., game theory and graph theory) do not offer an access control mechanism, due to which the existing schemes are computationally intensive and do not converge to offer a global optimum solution. Toward this goal, we proposed a whale optimization algorithm(WOA)-based access control scheme to enhance the performance of the resource allocation scheme in D2D communication. In WOA, we created a signal-to-interference-plus-noise ratio (SINR)-based objective function that iteratively discovers the best D2D users, allowing them to participate in the resource allocation process. Moreover, for resource allocation, we adopted the Munkres algorithm, which allows only optimized D2D users (from WOA) to reuse the resources of cellular users (CUs). In the proposed work, WOA acts as an access control scheme that optimally finds the best D2D users and only allows them to reuse cellular resources in the Munkres resource assignment problem. Simulation results show that the proposed scheme significantly improves the system's throughput compared to other existing algorithms. Moreover, other evaluation parameters, such as convergence rate, fairness, WOA update positions, and execution time, show the outperformance of the proposed scheme.

关键词： whale optimization algorithm evolutionary algorithm D2D communication optimization meta heuristic algorithm Munkres algorithm whale optimization algorithm evolutionary algorithm D2D communication optimization meta heuristic algorithm Munkres algorithm

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optimization of inspection and transit scheduling for imported refrigerated containers under public health emergencies

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JOURNAL OF SEA RESEARCH 2025年 205卷

作者： Ma, Qianli Wu, Wenbo Liu, Hongxiao Wang, Bo Li, Hao Dalian Maritime Univ Collaborat Innovat Ctr Transport Studies Dalian 116026 Peoples R China Dalian Maritime Univ Sch Maritime Econ & Management Dalian 116026 Peoples R China Guangdong Prov Transport Planning & Res Ctr Guangzhou 510040 Guangdong Peoples R China

The COVID-19 pandemic has highlighted the urgent need for policy-driven improvements in inspection protocols at international ports, particularly for refrigerated goods, which are vulnerable to virus transmission through external packaging. This study addresses the increased inspection demands during health emergencies, where inspection rates may rise from 10 % to 100 %, requiring a combination of advanced scanning technologies and meticulous manual checks. Such escalated inspection efforts inevitably lead to substantial logistical backlogs, exacerbating port congestion, prolonging dwell times, and increasing the risk of cargo spoilage due to temperature fluctuations. This study introduces a sophisticated scheduling model for container inspection operations, designed to align with current health policy frameworks. The model incorporates specific time windows and temperature controls to mitigate the risks associated with extended storage times. Furthermore, it examines the policy implications of varying the deployment of Automated Guided Vehicles (AGVs) and inspection platforms to optimize throughput and minimize operational delays during crises. Empirical results suggest that prioritizing the expansion of inspection infrastructure, coupled with strategic AGV adjustments, significantly enhances operational efficiency and ensures compliance with health policies during emergencies.

关键词： Port logistics Emergency Refrigerated container yard inspection platform Container inspection whale optimization algorithm

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A Two-Stage optimization Method for Multi-Runway Departure Sequencing Based on Continuous-Time Markov Chain

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AEROSPACE 2025年第4期12卷 273-273页

作者： Lian, Guan Wu, Yingzi Luo, Weizhen Li, Wenyong Zhang, Yaping Zhang, Xiaoyue Guilin Univ Elect Technol Guangxi Key Lab Intelligent Transportat Guilin 541004 Peoples R China Harbin Inst Technol Sch Transportat Sci & Engn Harbin 150006 Peoples R China Sichuan Airlines Create Engn & Technol Co Ltd Chengdu 610041 Peoples R China

With the rapid expansion of the aviation industry, traditional static scheduling methods have become inadequate to meet the increasingly complex demands of efficient airport operations. To enhance the operational efficiency of multi-runway airports, this paper introduced a two-stage dynamic departure scheduling method based on continuous Markov chains. The pushback rate control strategy was extended to multi-runway scenarios to identify the optimal taxiway queue threshold in stage I. In stage II, the pushback rate control strategy with a known queue threshold was introduced into a multi-objective optimization model, aiming to minimize flight delays and operational costs including pushback waiting times, taxi fuel consumption, and environmental impact. Then, continuous-time Markov chains (CTMC) were employed to track aircraft state transitions in the taxiway queue, and a nested whale optimization algorithm was proposed to optimize both the pushback sequence and runway resource allocation. Results indicate that the proposed method reduced the average taxiway queue time by 55.58%, with delay reductions of up to 73.06%, offering significant cost savings and environmental benefits while improving flight punctuality. This innovative approach highlights the potential for optimizing airport resource scheduling in complex and dynamic environments.

关键词： air traffic management departure scheduling pushback rate control continuous-time Markov chain whale optimization algorithm

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Stochastic modeling and availability optimization of reverse osmosis water purification system using metaheuristic algorithms

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JOURNAL OF QUALITY IN MAINTENANCE ENGINEERING 2024年第4期30卷 584-604页

作者： Saini, Monika Kumar, Naveen Sinwar, Deepak Kumar, Ashish Manipal Univ Jaipur Dept Math & Stat Jaipur India Manipal Univ Jaipur Dept IoT & Intelligent Syst Jaipur India

PurposeThe main objective of the present investigation is to develop a novel efficient stochastic model for availability optimization of reverse osmosis machine system (ROMS) for water purification under the concepts of exponentially distributed decision variables and various redundancy strategies at the component ***/methodology/approachROMS is a complex framework configured in a series structure using six subsystems. Initially, a state transition diagram is developed and Chapman-Kolmogorov differential-difference equations are derived using Markov birth death process. The steady-state availability of the ROMS is derived for a particular case. The impact of variation in failure and repair rates measured on availability. Furthermore, an effort is made to predict the optimal availability of the ROMS system using the metaheuristic algorithms, namely, dragonfly algorithm (DA), grasshopper optimization algorithm (GOA) and whale optimization algorithm (WOA).FindingsIt is observed that the ROMS system predicts optimal availability of 0.999926 after five iterations with a population size of 300 by the WOA. The findings of this study are significant for reliability engineers as well as for maintenance engineers to ensure the availability of ROMS for water ***/valueIn the present investigation, a novel stochastic model is developed for ROMS, and metaheuristics algorithms are applied to predict the optimal availability.

关键词： Stochastic modeling Availability optimization Reverse osmosis water purification Dragonfly algorithm whale optimization algorithm Grasshopper optimization algorithm

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Stochastic modeling and optimization of turbogenerator performance using meta-heuristic techniques

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QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL 2024年第8期40卷 4414-4438页

作者： Sinwar, Deepak Kumar, Naveen Kumar, Ashish Saini, Monika Manipal Univ Jaipur Dept IoT & Intelligent Syst Jaipur Rajasthan India Manipal Univ Jaipur Dept Math & Stat Jaipur Rajasthan India

The objective of this paper is to identify the most sensitive component of a turbogenerator and optimize its availability. To achieve this, we begin by conducting an initial reliability, availability, maintainability, and dependability (RAMD) analysis on each component. Subsequently, a novel stochastic model is developed to analyze the steady-state availability of the turbogenerator, employing a Markov birth-death process. In this model, failure and repair rates are assumed to follow an exponential distribution and are statistically independent. To optimize the proposed stochastic model, we employ four population-based meta-heuristic approaches: the grey wolf optimization (GWO), the dragonfly algorithm (DA), the grasshopper optimization algorithm (GOA), and the whale optimization algorithm (WOA). These algorithms are utilized to find the optimal solution by iteratively improving the availability of the turbogenerator. The performance of each algorithm is evaluated in terms of system availability and execution time, allowing us to identify the most efficient algorithm for this task. Based on the numerical results, it is evident that the WOA outperforms the GWO, GOA, and DA in terms of both system availability and execution time.

关键词： dragonfly algorithm grasshopper algorithm gray wolf optimization Markov model turbogenerator thermal power plant whale optimization algorithm

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