Multi-objective Whale Optimization Research Articles

Designing a new sustainable healthcare network considering the COVID-19 pandemic: Artificial intelligence-based solutions

The COVID-19 pandemic, which is still spreading its new mutations all around the world, is considered a healthcare challenge around the world. The best approach to forestall this pandemic is to avoid exposure to the virus. Therefore, medical protective equipment is essential for fighting this pandemic. This underlines the chief role of having a sustainable supply chain network (SCN) for producing and distributing personal protective equipment to avoid shortage and augmenting costs. The reality, however, is that the COVID-19 pandemic leads to many developments in countries and this study is another step for these purposes. This research developsa multi-period, multi-objective, multi-echelon, and multi-product medical protective equipment sustainable SCN considering the production, distribution, allocation, and inventory with “risk pooling” strategy effect with the aim of filling the existing gaps in health SCN research during the COVID-19 pandemic. By applying the risk pooling strategy, lower inventory levels or higher service levels can be achieved without increasing inventory costs. This model explores the possibility of lateral transshipments between distribution centers, as a way to increase the reliability of SCN performance. We model a new production, inventory, distribution, location, and allocation problem and consider four objectives for our suggested model (i) minimizing total SCN costs, (ii) minimizing environmental effects, (iii) minimizing social impacts, and (iv) maximizing the reliability of demand delivery. The proposedmodel simultaneously examines all three dimensions of sustainability (economic, environmental, and social) as well as the reliability of demand delivery. Considering all of these decisions and assumptions brings the studied problem closer to reality. We employ various algorithms for solving our developed model with different sizes: the improved version of the augmented ε-constraint (AUGMECON2) algorithm for small and medium-sized problems and two meta-heuristic algorithms, i.e., Multi-Objective Whale Optimization Algorithm (MOWOA) and Multi-Objective Variable Neighborhood Search (MOVNS) algorithm, for large-sized ones. The Taguchi approach is used to tune the parameters of meta-heuristic algorithms, and a comparison is performed using four evaluation metrics: Mean Ideal Distance (MID), Number of Pareto Solutions (NPS), Maximum Spread (MS), and Spread of Non-Dominance Solution (SNS). Proposed solving methods for the studied problem and making comparisons between them are another innovation of this study. A couple of numerical examples are provided to illustrate the applicability of the presented solution methods. Finally, sensitivity analysis for problem parameters is performed to validate our suggested model. Our study reveals the superiority of the MOWOA over the other algorithms.

RMOWOA: A Revamped Multi-Objective Whale Optimization Algorithm for Maximizing the Lifetime of a Network in Wireless Sensor Networks

RMOWOA: A Revamped Multi-Objective Whale Optimization Algorithm for Maximizing the Lifetime of a Network in Wireless Sensor Networks

Co-optimization of the operation and energy for AGVs considering battery-swapping in automated container terminals

Since the number of containers in automated container terminals (ACTs) has been growing, the operation of ACT relies on a large amount of electric power, which comes from the terminal energy system. As the main horizontal transportation equipment in ACTs, the scheduling of automated guided vehicles (AGVs) has become increasingly important. Even though AGV scheduling has been studied, the battery-swapping procedure is often overlooked, which hinders operation efficiency and the usage of renewable energy. We propose a co-optimization problem of the operation and energy for AGVs considering battery-swapping in this paper. A multi-objective model is constructed to minimize the makespan of AGVs and the operation and maintenance cost of energy system. In addition, we create a discrete multi-objective whale optimization algorithm to address this co-optimization problem. A load-balancing-based method for population initialization is developed to make the distribution of solutions more uniform in this algorithm. With the two-dimensional matrix encoding scheme, an individual right-shifted method is proposed for decoding. The best solution is assessed and selected through a fitness evaluation mechanism based on fuzzy correlation entropy. The bubble-net attack based on opposition-based learning is implemented to enhance the capacity for local intensification. Numerous tests confirm that the proposed method can guarantee the operation efficiency, lower the total cost of the energy system, and promote sustainable development in ACTs.

Binary Pareto Multi-Objective Whale Optimization Algorithm for Power Plant Maintenance Scheduling

Maintenance scheduling is a critical challenge in all major industrial sectors, such as aeronautics, automotive manufacturing, and power generation plants. The goal of power plant maintenance scheduling is to establish a schedule plan for carrying out preventive maintenance shutdowns for each unit within a defined planning horizon. In this paper, a mathematical model for optimizing the maintenance schedule was established to maximize the supply, minimize fuel costs, and CO2 emissions from generating units. A developed Whale Optimization Algorithm called Binary Pareto Multi-Objective Whale Optimization Algorithm BPMOWOA is proposed and implemented to find optimal maintenance scheduling for a power plant. The proposed algorithm uses two different approaches. The first approach includes binary encoding, in which each generating unit and each time interval are represented in binary form. Then, in the second approach, a fixed-sized repository is integrated into the WOA for saving and retrieving the Pareto optimal solutions, and a grid mechanism is integrated into the WOA to maintain diversity in the population of non-dominated solutions. A case study was conducted on fourteen generating units adapted from a real-world power plant to validate the algorithm's efficiency. The results illustrate that the proposed algorithm was effective in optimizing the maintenance schedule in terms of coverage and non-dominated solution and improved the power plant performance by increasing electricity generation by 11.48% and decreasing fuel expenses by 6.56%, which are the main goals of the considered power plant. Index Terms— maintenance scheduling, multi-objective, whale optimization algorithm, power plant.

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.

An improved learning automata based multi-objective whale optimization approach for multi-objective portfolio optimization in financial markets

An improved learning automata based multi-objective whale optimization approach for multi-objective portfolio optimization in financial markets

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.

Cloudlet Based Computing Optimization Using Variable-Length Whale Optimization and Differential Evolution

Cloudlet-based optimization involves deploying a set of cloudlets in an environment and assigning user tasks to optimize various metrics, including energy consumption, quality of service (QoS), and cost. Typically, approaches deal with them separately, which might cause sub-optimality. Furthermore, assuming the fixed location of the cloudlets will limit the dynamic adaptability of the problem. Enabling more optimality and adaptability to the dynamic nature of cloudlet-based computing, we propose a novel Variable-Length multi-objective Whale optimization Integrated with Differential Evolution designated as VL-WIDE. Unlike the existing optimization algorithm, VL-WIDE features the capability of searching different lengths of solutions to cover the variable number of cloudlets for deployment. Furthermore, it enables a non-dominated evaluation of solutions based on four objectives using crowding distance for selection. It provides an application-oriented solutions repair operator for repairing non-valid solutions and assuring that all solutions are generated in the feasible region. The proposed algorithm enables moving the cloudlets among pre-defined locations to increase the quality of service according to the change in the user density caused by user mobility. Comparing this developed algorithm with other algorithms shows its superiority in multi-objective optimization (MOO) evaluation metrics. VL-WIDE has provided the best in a number of non-dominated solutions and delta metrics and was competitive in other metrics.

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.

Spatial-temporal alignment of time series with different sampling rates based on cellular multi-objective whale optimization

Aligning time series of different sampling rates is an important but challenging task. Current commonly used dynamic time warping methods usually suffer from pathological temporal singularity problem. In order to overcome this, we transform the alignment task to a spatial-temporal multi-objective optimization (MOO) problem. Existing MOO algorithms are always inefficient in finding Pareto optimal alignment solutions due to their insufficiency in maintaining convergence and diversity among the obtained Pareto solutions. In light of this, we propose a novel and efficient MOO algorithm Cell-MOWOA which integrates Cellular automata with the rising Whale Optimization Algorithm to find Pareto optimal alignment solutions. Innovative multi-variate non-linear cell state evolutionary rules are designed within Pareto solution external archive to improve the convergence and diversity of the Pareto solutions, and novel whale population updating mechanism is designed to accelerate the convergence to the Pareto front. Besides, new integer whale updating mechanism is presented to transform real-number whale solutions to integer whale solutions. Experimental results on 85 gold-standard UCR time series datasets showed that Cell-MOWOA outperformed six state-of-the-art baselines by 24.53% in average in increasing alignment accuracy and 42.66% in average in reducing singularity. Besides, it achieved outstanding runtime efficiency, especially on long time series datasets.

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