Multi-objective Whale Optimization Algorithm Research Articles

A cloud-edge cooperative scheduling model and its optimization method for regional multi-energy systems

In the process of multi-energy system optimal scheduling, due to the high data processing requirements of the multi-energy devices and loads and the complexity of the operating states of the multi-energy devices, the scheduling optimization of the system is to some extent more difficult. To address this problem, this paper proposes a regional multi-energy system optimal scheduling model based on the theory of cloud-edge collaboration. First, based on intelligent data sensors, a cloud-edge cooperative scheduling framework of the regional multi-energy system is constructed. Then, the physical model of operating state data of multi-energy system equipment and the allocation mechanism of system scheduling tasks are studied. With the cloud service application layer and the edge computing layer as the upper and lower optimization scheduling layers, the double-layer optimization scheduling model of the regional multi-energy system is established. The objectives of the model are optimal scheduling cost and minimum delay of scheduling data transmission. The multi-objective whale optimization algorithm is used to solve the model. Finally, a simulation model is built for verification. The simulation results show that the scheduling model established in this paper can effectively improve the scheduling data processing capability and improve the economy of regional multi-energy system scheduling.

Multi Objective and Multi-Product Perishable Supply Chain with Vendor-Managed Inventory and IoT-Related Technologies

With the emergence of the fourth industrial revolution, the use of intelligent technologies in supply chains is becoming increasingly common. The aim of this research is to propose an optimal design for an intelligent supply chain of multiple perishable products under a vendor-managed inventory management policy aided by IoT-related technologies to address the challenges associated with traditional supply chains. Various levels of the intelligent supply chain employ technologies such as Wireless Sensor Networks (WSNs), Radio Frequency Identification (RFID), and Blockchain. In this paper, we develop a bi-objective nonlinear integer mathematical programming model for designing a four-level supply chain consisting of suppliers, manufacturers, retailers, and customers. The model determines the optimal network nodes, production level, product distribution and sales, and optimal choice of technology for each level. The objective functions are total cost and delivery times. The GAMS 24.2.1 optimization software is employed to solve the mathematical model in small dimensions. Considering the NP-Hard nature of the problem, the Grey Wolf Optimizer (GWO) algorithm is employed, and its performance is compared with the Multi-Objective Whale Optimization Algorithm (MOWOA) and NSGA-III. The results indicate that the adoption of these technologies in the supply chain can reduce delivery times and total supply chain costs.

Integration of resource supply management and scheduling of construction projects using multi-objective whale optimization algorithm and NSGA-II

Integration of resource supply management and scheduling of construction projects using multi-objective whale optimization algorithm and NSGA-II

Multi-Strategy Assisted Multi-Objective Whale Optimization Algorithm for Feature Selection

Multi-Strategy Assisted Multi-Objective Whale Optimization Algorithm for Feature Selection

Performance prediction of the centrifugal air compressor for fuel cells considering degradation characteristics based on the hierarchical evolutionary model

The performance of the centrifugal air compressor is closely related to the efficiency and energy density of the fuel cell system, and extensive work has been done in this study to achieve precise performance predictions of the compressor considering degradation characteristics. The durability test of the compressor was carried out for 5000 h, and degradation characteristics with break-in time were discussed in detail. On this basis, the hierarchical evolutionary model was constructed to perform the performance predictions based on the Light Gradient Boosting Machine (LightGBM) and Multi-objective Whale Optimization Algorithm (MOWOA). The results demonstrate that the outlet pressure of the compressor decreases by 9.4 kPa and the power consumption increases by 0.71 kW at rated volume flow rate after the break-in of 5000 h, with attenuation ratios of 4.7 % and 12.2 %, respectively. The final non-dominated solution sets have excellent uniformity, diversity and convergence, proving that the developed MOWOA has high adaptability and strong optimization capability. The hierarchical evolutionary model achieves exact predictions for the outlet temperature, flow rate and pressure of the compressor, with absolute relative errors (AREs) below 1 % in outlet temperature and pressure while 5 % in outlet flow rate. All of these provide an efficient multi-objective optimization algorithm and an auto-calibrated hierarchical evolutionary model for further performance optimization of the air compressor.

Optimization of a solar-based PEM methanol/water electrolyzer using machine learning and animal-inspired algorithms

This contribution scrutinizes the performance of the integrated photovoltaic thermal (PVT) system with the proton exchange membrane methanol electrolyzer (PEMME) and water electrolyzer (PEMWE) as sustainable ways to produce hydrogen. Artificial neural networks (ANNs), are adopted to evaluate the effect of various operating parameters on the performance of the systems. The adopted ANNs are radial basis function (RBF), extreme learning machine (ELM), long short-term memory (LSTM), and gated recurrent unit (GRU). Moreover, to obtain the optimum performances of the systems, the multi-objective whale optimization algorithm (MOWOA) and multi-objective bat algorithm (MOBA) are implemented. It is found that solar radiation is the most influential parameter on the hydrogen production rate of the systems, followed by ambient temperature, inlet temperature, working fluid mass flow rate, and wind speed. According to the machine learning outputs, the highest hydrogen production rate for the PVT-PEMWE and PVT-PEMME systems are around 2.45 mol h−1 m−2 and 5.44 mol h−1 m−2, respectively. Moreover, the MOBA shows that the hydrogen production rate and electrical efficiency of the PVT-PEMWE and PVT-PEMME systems are 2.29 mol h−1 m−2 and 18.74%, and 5.06 mol h−1 m−2, and 18.77%, respectively, at their global optimum point by considering hydrogen production rate and electrical efficiency as the objective functions. This study reveals that although at the same working conditions, the electrical and thermal output of the water-based PVT system is superior to that of the water/methanol-based PVT system, the PVT-PEMME system outperforms the PVT-PEMWE system from the hydrogen production viewpoint, due to the higher efficiency of the PEM methanol electrolyzer.

Multi-objective two-stage emergent blood transshipment-allocation in COVID-19 epidemic

The problem of blood transshipment and allocation in the context of the COVID-19 epidemic has many new characteristics, such as two-stage, trans-regional, and multi-modal transportation. Considering these new characteristics, we propose a novel multi-objective optimization model for the two-stage emergent blood transshipment-allocation. The objectives considered are to optimize the quality of transshipped blood, the satisfaction of blood demand, and the overall cost including shortage penalty. An improved integer encoded hybrid multi-objective whale optimization algorithm (MOWOA) with greedy rules is then designed to solve the model. Numerical experiments demonstrate that our two-stage model is superior to one-stage optimization methods on all objectives. The degree of improvement ranges from 0.69 to 66.26%.

Optimal DG unit placement in distribution networks by multi-objective whale optimization algorithm & its techno-economic analysis

• The majority of academics focused more on Distributed Generations (DG) ideal siting and size with less research being done on economic studies. • The economic analysis of the ideal sizing and placement of distributed generation is also viewed as a contribution to this work. In addition to technical analysis of the placement and sizing of DGs, economic analysis is performed as an added advantage. • Additionally, the majority of the research was concentrated on placing a single DG; however, in this work simultaneous installation of DGs at various sites was carried out and its techno-economic analysis was examined. • Furthermore, simultaneous multi-objective formulation tasks have included assessing annual economic losses by positioning DGs optimally and minimizing real power loss. In radial distribution networks, the appropriate placement of properly sized Distributed Generation (DG) units can significantly improve the performance of the system. The biggest techno-economic benefits can be obtained by reducing annual economic losses that include the expenses of deployment, operation and maintenance along with voltage variations and power loss across the buses. The current problem is examined in light of various multi-objective frameworks as well as the optimum compromise solution also termed the Pareto-optimal solution is presented. When dealing with a multi-objective optimization problem, certain constraints on equality and inequality are also examined. The focus of this paper is on a one-of-a-kind multi-objective whale optimization (MOWOA) algorithm for multi-objective problem-solving. To test its effectiveness, the method that was suggested is implemented on radial bus distribution systems IEEE-33 and IEEE-69. This paper also includes a comparison with other recent multi-objective algorithms such as opposition-based chaotic differential evolution (OCDE), Krill herd algorithm (KHA) and Power Loss Sensitivity Factor and Simulated Annealing (LSFSA). It has been discovered that the method proposed may improve power loss, annual economic loss mitigation and voltage profile improvement.

Defense against SSDF Attack and PUE Attack in CR-Internet of Vehicles (IoVs) for Millimeter Wave Massive MIMO Beamforming Systems

The Internet of Vehicles (IoV) is witnessed to play the leading role in the future of Intelligent Transportation Systems (ITS). Though many works have focused on IoV improvement, there is still a lack of performance due to insufficient spectrum availability, lower data rates, and the involvement of attackers. This paper considers all three issues by developing a novel mmWave-assisted Cognitive Radio based IoV (CR-IoV) model. The integration of CR in IoV resolves the issue of spectrum management, while mmWave technology ensures symmetry in acquiring higher data rates for Secondary Users (SUs). With the proposed mmWave-assisted CR-IoV model, symmetric improvements in network performance were achieved in three main areas: security, beamforming, and routing. Optimum detection mechanisms isolate malicious Secondary Users (SUs) in the overall network. First, Spectrum Sensing Data Falsification (SSDF) attack is detected by a Hybrid Kernel-based Support Vector Machine (HK-SVM), which is the lightweight Machine Learning (ML) technique. Then, the Primary User Emulation (PUE) attack is detected by a hybrid approach, namely the Fang Algorithm-based Time Difference of Arrival (FA-TDoA) method. Further, security is assured by validating the legitimacy of each SU through a Lightweight ID-based Certificate Validation mechanism. To accomplish this, we employed the Four Q-curve asymmetric cryptographic algorithm. Overall, the proposed dual-step security provisioning approach assures that the network is free from attackers. Next, beamforming is performed for legitimate SUs by a 3D-Beamforming algorithm that relies on Array Factor (AF) and Beampattern Function. Finally, routing is enabled by formulating Forwarding Zone (FZ) based on the forwarding angle. In the forwarding zone, optimal forwarders are selected by the Multi-Objective Whale Optimization (MOWO) algorithm. Here, a new potential score is formulated for fitness evaluation. Finally, the proposed mmWave-assisted CR-IoV model is validated through extensive simulations in the ns-3.26 simulation tool. The evaluation shows better performance in terms of throughput, packet delivery ratio, delay, bit error rate, and detection accuracy.

A coupling optimization method of production scheduling and computation offloading for intelligent workshops with cloud-edge-terminal architecture

With the rapid development and application of Industrial Internet of Things (IIoT) technology, the amount of data generated during the operations of intelligent workshops shows an explosive growth trend. Real-time analysis and processing of these data is the premise of rapid intelligent decision-making. Therefore, the cloud-edge-terminal (CET) architecture is usually adopted in manufacturing sites, and the overall delay of data processing can be reduced by designing a reasonable offloading strategy for computing tasks. In general, computing tasks are generated by demand-driven monitoring, measurement, analysis, and decision-making during the executions of production jobs. There is a strong coupling relationship between them, and the scheduling scheme of production jobs largely determines the generation sequence and processing requirements of computing tasks. However, the current optimization methods for production scheduling and computation offloading are carried out separately to pursue their own goals, resulting in that the balance between production efficiency and computing delay cannot be achieved. To this end, this paper proposes a Production scheduling-Computation offloading Coupling Optimization (PCCO) model for intelligent workshops with CET architecture for the first time, which takes the maximum completion time of production jobs and the minimum total offloading delay time of computing tasks as dual optimization objectives. Then a four-layer coding method is designed and an enhanced multi-population multi-objective whale optimization algorithm (EMMOWOA) is proposed to solve the PCCO model. In EMMOWOA, the arithmetic exploration mechanism is adopted to strengthen the exploration ability, and a new leader selection method is proposed to lead the search of population to avoid prematurely trapping into the local optimum; to avoid the loss of diversity, an adaptive multi-population strategy is adopted. A series of comparative experimental results based on multi-objective standard test functions show that the proposed EMMOWOA has better convergence and diversity than the newly proposed multi-objective optimization algorithms, which proves the effectiveness of the proposed algorithm improvement strategies. Finally, simulation cases and a practical production case prove that the proposed coupling optimization method can balance production efficiency and computing delay well, and better meet the optimization requirements of intelligent manufacturing sites.

An Adaptive Multiobjective Multitask Service Composition Approach Considering Practical Constraints in Fog Manufacturing

Fog manufacturing (FMfg) is an emerging real-life cloud manufacturing (CMfg) paradigm based on digital twin models, which perceive finely dynamic processes and provide on-demand optimization services. Service composition as a core plays a vital role in achieving the optimization goal. Service composition issues in actual CMfg are mostly multitask problems that need considering dynamic factors. Otherwise, the planned production process will deviate significantly from actual situations. However, such multitask service composition (MTSC) models have scarcely been studied. Thus, this article formulates a multiobjective MTSC model considering the crucial resource competition process between multitasks under multiple practical constraints (MPC). Then, to solve the MTSC–MPC, this article develops an adaptive multiobjective whale optimization algorithm (AMOWOA) containing well-designed strategies. Numerical experiments and application cases verify that AMOWOA outperforms the comparison algorithms. AMOWOA continually can optimize and adaptively adjust the service composition process based on the crucial manufacturing resource available time under actual constraints in FMfg environment.

An efficient multi-objective gorilla troops optimizer for minimizing energy consumption of large-scale wireless sensor networks

The multi-objective gorilla troops optimizer (MOGTO) is a new version of the gorilla troops optimizer (GTO) proposed in this paper to address multi-objective optimization issues. The Pareto optimum solutions acquired by the GTO are saved in an external archive. In the multi-objective search region, the archive was used to mimic the gorilla groups’ collective behavior. The suggested approach is evaluated statistically and qualitatively in solving various multi-objective issues using the congress on evolutionary computation (CEC) 2020 test bed. In large-scale wireless sensor networks, the proposed algorithm is also utilized to discover the minimal number of sink nodes with the lowest localization error, which will cap the whole network and increase the network lifespan. Meanwhile, the multi-objective particle swarm optimization (MOPSO), non-dominated sorting genetic algorithm version 2 (NSGA-II), multi-objective grey wolf optimizer (MOGWO), multi-objective whale optimization algorithm (MOWOA), multi-objective sine–cosine algorithm (MOSCA), multi-objective slime mould algorithm (MOSMA), multi-objective particle swarm optimization with ring topology and special crowding distance (MO_Ring_PSO_SCD), hybrid NSGAII-MOPSO, multi-objective evolutionary algorithm based on decomposition (MOEA/D), and improved multi-objective manta ray foraging optimization (IMOMRFO) are the ten familiar and strong optimization models, which are compared with the proposed algorithm. Simulation results in CEC’2020 test functions indicated that the proposed MOGTO can provide remarkable results than other optimization models in terms of Pareto set proximity (PSP), inverted generational distance in decision space (IGDX), and hyper volume (HV) indicators. Additionally, simulation results in large-scale wireless sensor networks show that the proposed algorithm can discover the smallest number of sink nodes and diminish the network’s energy usage.

New network interface selection based on MADM and multi-objective whale optimization algorithm in heterogeneous wireless networks

New network interface selection based on MADM and multi-objective whale optimization algorithm in heterogeneous wireless networks

Pareto-Optimal Design of Litz-Wire Gapped-Core High-Frequency Transformer for LLC Converters

The optimization of Litz-wire gapped-core high-frequency transformer (LGHT) for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LLC</i> converters is a challenging task owing to application requirements and complex relationship between multiple design-objectives and constraints. This necessitates the use of multiobjective optimization-techniques to obtain tradeoff design-solutions; however, these are cumbersome methods that require many iterations and resources. Therefore, this article proposes an improved and efficient Pareto-optimization method for LGHT-design. It combines multiobjective whale-optimization algorithm and analytical core-geometry factor model based core and winding selection method while keeping in consideration the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LLC</i> converter requirements. The proposed method utilizes benefits of both method-types; it obtains the best LGHT-design in shortest-time and minimum-iterations with optimized volume and losses, integrated magnetizing-inductance, and satisfied thermal-limit. The method is made resource-efficient by carefully selecting independent and necessary LGHT design-parameters as optimization variables keeping in view interdependency among the parameters and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LLC</i> converter performance; these include peak-flux-density, LGHT design-frequency, core-window utilization-factor, core-material, and core-shape. For best design-selection, supplementary decision functions (total-loss and loss-and -volume-product) are used together with the objective functions (core-loss, winding-loss, and total-volume) to enhance the optimal Pareto-solutions’ diversity and to assist designers in decision-making. The method is proven via comparison with existing benchmark-methods and validated through performance demonstration of LGHT-prototype in 400 Vdc-to-12 Vdc <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LLC</i> converter.

Sizing of centralized shared energy storage for resilience microgrids with controllable load: A bi-level optimization approach

To improve the utilization of flexible resources in microgrids and meet the energy storage requirements of the microgrids in different scenarios, a centralized shared energy storage capacity optimization configuration model for microgrids based on bi-level optimization is proposed. First, the response characteristics of the shared energy storage and controllable load in the resilience microgrid are analyzed, and the centralized shared energy storage operation mode meeting the regulatory demand of multi-scenarios is designed. Then, a bi-level optimal allocation model is constructed, which takes the maximum net income of centralized shared energy storage as the upper layer and the minimum payment cost of load in the microgrid as the lower layer. Furthermore, the multi-objective whale optimization algorithm is used to solve the bi-level optimization model. The results show that the shared energy storage can jointly meet the regulation demand of multi-scenarios by coordinating the transferable load and cuttable load in the microgrid and improving the utilization rate of shared energy storage.

Balancing thermal comfort and energy conservation– A multi-objective optimization model for controlling air-condition and mechanical ventilation systems

Building air condition and mechanical ventilation (ACMV) systems which provide cooling operations suffers from the balance between thermal comfort (TC) and energy consumption (EC). This study presents a multi-objective optimization model that resolves the trade-off between TC and EC. Four operating variables are selected as decision variables because they significantly affect TC and EC. Firstly, the component power consumption pattern of the ACMV system (that is, fan, pump, and compressor) is built by a semi-empirical model. Then EC is modeled as a function of the component power consumption pattern and air temperature. Secondly, TC is modeled as a function of environmental, personal, and human-robot collaboration variables. Thirdly, an artificial neural network (ANN) was constructed as a prediction model of TC and EC. Then, ANN is coupled with multi-objective whale optimization algorithm for optimization of decision variables. Sensitivity analysis was carried out to examine the variation of decision variables. Experiment result shows that (1) the proposed ANN for TC and EC exhibits high performance with a goodness-of-fit of 98.83% and 98.25% respectively. (2) The optimization model reduces a 16.51% of EC and improves TC by 49.06% as compared to the reference operation. This study successfully builds a multi-objective optimization model of an ACMV system for TC and EC.

Improved whale optimization algorithm and its application in vehicle structural crashworthiness

Whale optimization algorithm (WOA) is a novel-innovative swarm-based meta-heuristic algorithm with excellent performance, but it may still be trapped into local extremum for troublesome problems. To this end, an improved multi-objective whale optimization algorithm (IMOWOA) is proposed to cover the shortages. Firstly, in the search stage of WOA, individual difference is considered to strengthen the exploration ability, and evolution operators are introduced to regenerate the stagnated population to prevent premature convergence. Next, the performance of IMOWOA is compared with MOWOA and other classical optimization algorithms, and a series of multi-objective test functions are used. The results on the convergence and diversity of Pareto front confirm that IMOWOA has better feasibility and competitiveness. Finally, integrated with the least squares support vector regression (LSSVR) model, IMOWOA is applied to the deterministic optimization of vehicle structural crashworthiness. The conclusion testified the efficiency of IMOWOA in the field of vehicle crashworthiness.

Cooperative multi-function approach: A new strategy for autonomous ground robotics

The paper presents multi-objective optimization technique for the exploration of unknown space. Exploration refers to the building of an estimated finite map of the environment using sensor data. Conventionally, in robotics, the optimization is performed utilizing a single optimization technique with a particular objective function. This although simplifies the process but greatly compromise the map accuracy and exploration depth. Realizing this aspect, we present a new exploration technique with augmented objective functions, so as maximize the search area and map accuracy of the exploration space. The proposed framework termed as Multi-Objective Whale Optimization Algorithm (MO-WOA), is based on bio-inspired Whale Optimizer. It starts with the initialization of the whale’s population, which are referred to as way-points. These way-points are assumed to be constant once they are set in the initial stage/iterations. The next step involves the position update from the non-dominated way-points catered by the robots. The algorithm utilizing this optimization approach formulates the optimal way-points. The performance metrics are presented through extensive simulations. The results efficacy is then demonstrated by comparing the results of the proposed algorithm with those achieved from contemporary techniques namely Coordinated Multi-Robot Exploration (CME), conventional Whale Optimizer (WO) integrated with CME and Arithmetic Optimization (AO). Results demonstrate that the proposed algorithm significantly improved the exploration parameters by enhancing the explored area and reducing the search time.

Blockchain enabled joint trust (MF‐WWO‐WO) algorithm for clustered‐based energy efficient routing protocol in wireless sensor network

AbstractIn this manuscript, a blockchain enabled joint trust (MF‐WWO‐WO) algorithm for clustered based energy efficient routing protocol in wireless sensor network (WSN) is proposed for secure data transmission by finding the optimum cluster head (CH) in the network. Here, MayflyWater Wave Optimization (MF‐WWO) algorithm is used for CH selection accurately. After the selection of CH, the malicious node occurs in the cluster. Hence, the multi objective whale optimization algorithm is proposed to find the trust path from the several paths. At last, the optimized selected trust paths are given to the blockchain for communications in the network that is safe and more trustworthy. The simulation is done in MATLAB. The proposed MF‐WWO‐WO achieves high throughput, high efficiency, high network life time, high packet delivery ratio, low delay, low energy consumption, and low packet loss ratio. The outcomes shows the better performance of the proposed approach when compared to the existing approaches, like blockchain and clustered based secured data aware energy efficient using fitness averaged rider optimization algorithm FA‐ROA blockchain and clustered based secured data aware energy efficient using trusted moth flame optimization and genetic optimization algorithm TMFO‐GOA. Finally, the proposed approach attains better energy efficiency and security.

An Adaptive Classifier Based Approach for Crowd Anomaly Detection

Crowd Anomaly Detection has become a challenge in intelligent video surveillance system and security. Intelligent video surveillance systems make extensive use of data mining, machine learning and deep learning methods. In this paper a novel approach is proposed to identify abnormal occurrences in crowded situations using deep learning. In this approach, Adaptive GoogleNet Neural Network Classifier with Multi-Objective Whale Optimization Algorithm are applied to predict the abnormal video frames in the crowded scenes. We use multiple instance learning (MIL) to dynamically develop a deep anomalous ranking framework. This technique predicts higher anomalous values for abnormal video frames by treating regular and irregular video bags and video sections. We use the multi-objective whale optimization algorithm to optimize the entire process and get the best results. The performance parameters such as accuracy, precision, recall, and F-score are considered to evaluate the proposed technique using the Python simulation tool. Our simulation results show that the proposed method performs better than the conventional methods on the public live video dataset.