Multi-objective Whale Optimization Algorithm Research Articles

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.

Opposition-based multi-objective whale optimization algorithm with multi-leader guiding

During recent decades, evolutionary algorithms have been widely studied in optimization problems. The multi-objective whale optimization algorithm based on multi-leader guiding is proposed in this paper, which attempts to offer a proper framework to apply whale optimization algorithm and other swarm intelligence algorithms to solving multi-objective optimization problems. The proposed algorithm adopts several improvements to enhance optimization performance. First, search agents are classified into leadership set and ordinary set by grid mechanism, and multiple leadership solutions guide the population to search the sparse spaces to achieve more homogeneous exploration in per iteration. Second, the differential evolution and whale optimization algorithm are employed to generate the offspring for the leadership and ordinary solutions, respectively. In addition, a novel opposition-based learning strategy is developed to improve the distribution of the initial population. The performance of the proposed algorithm is verified in contrast to 10 classic or state-of-the-arts algorithms on 20 bi-objective and tri-objective unconstrained problems, and experimental results demonstrate the competitive advantages in optimization quality and convergence speed. Moreover, it is tested on load distribution of hot rolling, and the result proves its good performance in real-world applications. Thus, all of the aforementioned experiments have indicated that the proposed algorithm is comparatively effective and efficient.

Adaptive multi-objective service composition reconfiguration approach considering dynamic practical constraints in cloud manufacturing

Dynamic uncertainty factors such as equipment faults are common in practically implemented cloud manufacturing (CMfg) environments, often causing the manufacturing service to be invalidated. In that case, efficient reconfiguration of the original service composition under practical constraints is critical; however, existing research scarcely focuses on it. This paper proposes a dynamic service composition reconfiguration model to bridge the gap by considering practical constraints (DSCRPC) in a real-life cloud manufacturing environment. Based on the constraints considered in this study, the DSCRPC model redefines three objectives: time (T*), cost (C*), and product service quality (Q*S*). To optimize the DSCRPC model, this study developed an adaptive multi-population multi-objective whale optimization algorithm (AMPOWOA) based on the Pareto strategy. The algorithm adopts four balancing strategies and adaptively optimizes and adjusts the key parameters under various balancing strategies through well-designed reinforcement learning models. Finally, we conduct numerical experiments and actual application case tests to compare the performances of AMPOWOA and other algorithms (MOWOA, MOHHO, NSGA-II). The results show that DSCRPC can continuously tackle the cloud manufacturing service composition (CMSC) reconfiguration issue with constraints until an order is completed. Moreover, AMPOWOA is superior to the other algorithms optimizing the DSCRPC model. This significantly enhances the robustness of service composition reconfiguration in real-life CMfg.

An efficient slime mould algorithm for solving multi-objective optimization problems

Recently, the Slime mould algorithm (SMA) was proposed to solve the single-objective optimization problems. It is considered as a strong algorithm for its efficient global search capability. This paper presents a multi-objective optimization algorithm based on the SMA called multi-objective SMA (MOSMA). An external archive is utilized with the SMA to store the Pareto optimal solutions obtained. The archive applied to emulate the social behaviour of the slime mould in the multi-objective search space. The performance of the MOSMA is validated on the CEC’20 multi-objective benchmark test functions. Furthermore eight well-known of constrained and unconstrained test cases, four constrained engineering design problems are tested to demonstrate the MOSMA superiority. Moreover, the real-world multi-objective optimization of helical coil spring for automotive application to depict the reliability of the presented MOSMA to solve real-world problems. Over the statistical side, the Wilcoxon test and performance indicators are used to assess the effectiveness of MOSMA against six well-known and robust optimization algorithms: multi-objective grey wolf optimizer (MOGWO), multi-objective particle swarm optimization (MOPSO), multi-objective salp swarm algorithm (MSSA), Non-dominated sorting genetic algorithm version 2 (NSGA-II), multi-objective whale optimization algorithm (MOWOA) and strength Pareto evolutionary algorithm 2 (SPEA2). The overall simulation results reveal that the proposed MOSMA has the ability to provide better solutions as compared to the other algorithms in terms of Pareto sets proximity (PSP) and inverted generational distance in decision space (IGDX) indicators.

Capacity‐basedoptimization using whale optimization technique of a power distribution network

AbstractIn the power distribution network, real power loss and voltage profile management are critical issues. By providing active and reactive power support, both of these issues can be managed. Distributed generation (DG) and capacitor bank (QG) can be utilized to solve these issues. Therefore, this paper utilized optimally placed and sized DG and capacitor (QG) to minimize losses and improve the voltage profile. The overall problem is optimized using an upgraded method of the fitness assignment and solution chasing based on the aggregate approach called multi‐objective whale optimization algorithm (MWOA). Wind and solar photovoltaic sources with biomass are utilized as the DG sources with their probabilistic outputs. The developed method is tested using two practical feeders of Bahir Dar city distribution network, Ethiopia. The results of loss minimization and voltage profile enhancement with MWOA are compared with multi‐objective particle swam optimization (MPSO) with an equal number of iterations to show the superiority of the developed method.

Multi-Objective Whale Optimization Algorithm for Computation Offloading Optimization in Mobile Edge Computing.

Computation offloading is one of the most important problems in edge computing. Devices can transmit computation tasks to servers to be executed through computation offloading. However, not all the computation tasks can be offloaded to servers with the limitation of network conditions. Therefore, it is very important to decide quickly how many tasks should be executed on servers and how many should be executed locally. Only computation tasks that are properly offloaded can improve the Quality of Service (QoS). Some existing methods only focus on a single objection, and of the others some have high computational complexity. There still have no method that could balance the targets and complexity for universal application. In this study, a Multi-Objective Whale Optimization Algorithm (MOWOA) based on time and energy consumption is proposed to solve the optimal offloading mechanism of computation offloading in mobile edge computing. It is the first time that MOWOA has been applied in this area. For improving the quality of the solution set, crowding degrees are introduced and all solutions are sorted by crowding degrees. Additionally, an improved MOWOA (MOWOA2) by using the gravity reference point method is proposed to obtain better diversity of the solution set. Compared with some typical approaches, such as the Grid-Based Evolutionary Algorithm (GrEA), Cluster-Gradient-based Artificial Immune System Algorithm (CGbAIS), Non-dominated Sorting Genetic Algorithm III (NSGA-III), etc., the MOWOA2 performs better in terms of the quality of the final solutions.

A novel bi-level robust game model to optimize a regionally integrated energy system with large-scale centralized renewable-energy sources in Western China

To improve the economics and scheduling flexibility of regionally integrated energy system (RIES) with large-scale centralized renewable energy, a bi-level robust game model is proposed. The model relies on the CCHP units and virtual energy-plant, uses their complementary of various energy to stabilize the uncertainty of power generation and consumption. The revenue and loss risk for coupling multiple uncertain is considered, and the uncertain set is constructed by robust theory, which can be flexibly adjusted its boundaries. Aim at the above-mentioned complex model, such as multi-objective, nonconvex, strong constraint, large-scale decision variables, and irregular Pareto front shape, a multi-objective whale optimization algorithm is proposed. Finally, a modified IEEE-33 bus distribution system and the Hami power grid (in Western China) are used for cases. The sensitivity for scheduling results is quantitatively analyzed, under the influence of power generation and consumption uncertainty. The results show that the strategy can balance the economy and robustness, improve the profit benefit of RIES by 15.67% with the traditional robustness, virtual energy-plant can increase the profit by 1.9%. This paper provides theoretical guidance for the optimal scheduling of RIES with large-scale renewable energy.

Operation of the of Golestan Dam Reservoir in Climate Change Conditions Using an Improved Multi-Objective Whale Optimization Algorithm

Operation of the of Golestan Dam Reservoir in Climate Change Conditions Using an Improved Multi-Objective Whale Optimization Algorithm

A hybrid multi-objective whale optimization algorithm for analyzing microarray data based on Apache Spark.

A microarray is a revolutionary tool that generates vast volumes of data that describe the expression profiles of genes under investigation that can be qualified as Big Data. Hadoop and Spark are efficient frameworks, developed to store and analyze Big Data. Analyzing microarray data helps researchers to identify correlated genes. Clustering has been successfully applied to analyze microarray data by grouping genes with similar expression profiles into clusters. The complex nature of microarray data obligated clustering methods to employ multiple evaluation functions to ensure obtaining solutions with high quality. This transformed the clustering problem into a Multi-Objective Problem (MOP). A new and efficient hybrid Multi-Objective Whale Optimization Algorithm with Tabu Search (MOWOATS) was proposed to solve MOPs. In this article, MOWOATS is proposed to analyze massive microarray datasets. Three evaluation functions have been developed to ensure an effective assessment of solutions. MOWOATS has been adapted to run in parallel using Spark over Hadoop computing clusters. The quality of the generated solutions was evaluated based on different indices, such as Silhouette and Davies–Bouldin indices. The obtained clusters were very similar to the original classes. Regarding the scalability, the running time was inversely proportional to the number of computing nodes.

An improved multi-objective whale optimization algorithm for the hybrid flow shop scheduling problem considering device dynamic reconfiguration processes

Manufacturing industries frequently encounter production scheduling problems containing device dynamic reconfiguration processes (DRP). DRP refers to dynamic device adjustments (such as replacement of tools), leading to changes in the devices’ actual processing time. It has a severe impact on the production schedule. Nevertheless, there is scarcely research upon hybrid flow shop scheduling problem (HFSP) with DRP. Besides, it is necessary to consider multiple conflict objectives in the HFSP. Thus, the multi-objective HFSP with DRP (MOHFSP-DRP) is significant in both theoretical research and application. This paper first proposes a multi-objective mathematical model (MOHFSP-DRP) that simultaneously considers the DRP and devices’ adjustable processing modes. The bi-objective of this model is to minimize both the makespan and the whole device’s energy consumption. This study then proposes an improved multi-objective whale optimization algorithm (IMOWOA) to solve the MOHFSP-DRP and obtain the Pareto-based optimal solution set. After that, to verify the proposed method’s effectiveness, numerical experiments are implemented based on the real-world cases in a Chinese company’s digital hot-rolling workshop. Results denote that the presented IMOWOA is superior to SPEA2 and NSGA-II. Finally, the MOHFSP-DRP model and IMOWOA are applied to a real-world hot-rolling shop successfully. The real-world cases verify the proposed IMOWOA can tackle the presented MOHFSP-DRP very well.

Hybrid time-scale energy optimal scheduling strategy for integrated energy system with bilateral interaction with supply and demand

In view of the complex energy coupling and multiple market entities competition in integrated energy system, a hybrid time-scale energy optimal scheduling model based on game interaction with supply and demand is presented. Considering the initiative of market players in the day-ahead scale, a bilateral game interaction framework for the operator of integrated energy system and a calibration model considering differences in energy distinguishing feature in the intraday scale are presented. At the intraday scale, the cold and thermal energy related equipment and controllable resources with low time resolution were modified combined with the day-ahead plan in slow layer, while the power related’s with high time resolution were modified combined with the slow layer plan in fast layer. Aim at the above-mentioned complex model, such as multi-objective, nonconvex, strong constraint, large-scale decision variables and irregular Pareto front shape, an adaptive reference point based large scale multi-objective whale optimization algorithm is proposed. Finally, the bilateral interactivity and sensitivity for scheduling results were quantitatively analyzed, under the influence of power generation and consumption uncertainty. The results show that the strategy can balance the economy and robustness, improve the profit benefit of IES by 61.5% with the traditional robustness. Balance the benefits of market entities, bilateral game can increase the benefit of IES by 57.6%. The model based on different energy characteristics is more in line with the actual scheduling situation.

Photovoltaic and Electric Vehicle-to-Grid Strategies for Peak Load Shifting in Low Voltage Distribution System Under Time of Use Grid Pricing

Contemplating the alarming increase in emission of greenhouse gases and global energy demand, electric vehicles (EVs) powered by both the grid and solar photovoltaic (PV) systems pose an effective solution. PV arrays reduce the dependence of EVs on the utility grid for charging. As EVs store energy, they can apportion power to fulfill load demands during peak hours. This paper evaluates the impacts of integrating EVs and PV arrays in distribution networks by evaluating their effects in a given system, during a pre-characterized period with controlled charging and discharging strategy. It simulates EVs’ movement in a geographic region by considering a case study-based EV travelling pattern. Adaptive modified multi-objective whale optimization algorithm (A-MWOA) is utilized not exclusively to limit the effect of EV charging/releasing on the network yet, in addition, to reduce the expenses borne by both the EV proprietor and the service provider. It estimates the maximum number of EVs and PV arrays that can be securely incorporated in a given system and the progressions incited by EVs in the load diagrams, voltage profiles, line loading and energy losses are analyzed. The results validate the proposed model from the perspective of practical applications.

Multi-objective whale optimization algorithm and multi-objective grey wolf optimizer for solving next release problem with developing fairness and uncertainty quality indicators

Selecting a set of requirements to implement in the next software release is an NP-Hard problem known as NRP. We propose multi-objective versions of grey wolf optimizer and whale optimization algorithm for solving bi-objective NRP. We used these two algorithms and three other evolutionary algorithms to solve NRP problem instances from four datasets. The cost-to-score ratio and the roulette wheel are used to satisfy constraints of the NRP problem. We compare obtained Pareto fronts based on eight quality indicators. In addition to four general multi-objective optimization quality indicators, the three aspects of fairness among clients and also uncertainty are reconfigured as quality indicators. These quality indicators are computed for a Pareto front. Results show that MOWOA performs better than others and makes requirement selection fairer. MOGWO works better than the rest when budget constraints are reduced.

Category-Based Multiobjective Approach for Optimal Integration of Distributed Generation and Energy Storage Systems in Distribution Networks

Distributed generation (DG) units are power generating plants that are very important to the architecture of present power system networks. The primary benefits of the addition of these units are to increase the power supply and improve the power quality of a power grid while considering the investment cost and carbon emission cost. Most studies have simultaneously optimized these objectives in a direct way where the objectives are directly infused into the multiobjective framework to produce final values. However, this method may have an unintentional bias towards a particular objective; hence this paper implements a multi-stage framework to handle multiple objectives in a categorical manner to simultaneously integrate DG units and Battery Energy Storage System (BESS) in a distribution network. A new hybrid metaheuristic technique is developed and combined with the Technique Order for Preference by Similarity to Ideal Solution (TOPSIS) approach and the crowding distance technique to produce Pareto optimal solutions from the multiple collective objectives, namely technical, economic, and environmental. Compared to the conventional direct way approach in multiobjective handling, the proposed categorical approach reduces bias towards a set of objective(s) and efficiently handles more objectives. Results also show that the Whale Optimization Algorithm and Genetic Algorithm (WOAGA) produces the smallest power loss of 101.6 kW compared to Whale Optimization Algorithm (WOA) and Genetic Algorithm (GA), which produces 105.1 kW and 105.8 kW respectively. The algorithm, although does not have a faster convergence than the WOA, has a better computational time than the WOA and GA. The multiobjective WOAGA also performs better than the Non-dominating Sorted Genetic Algorithm (NSGA-II) and the multiobjective WOA in terms of the quality of Pareto optimal solutions.

A competitive mechanism integrated multi-objective whale optimization algorithm with differential evolution

In this paper, a competitive mechanism integrated whale optimization algorithm (CMWOA) is proposed to deal with multi-objective optimization problems. By introducing the novel competitive mechanism, a better leader can be generated for guiding the update of whale population, which benefits the convergence of the algorithm. It should also be highlighted that in the competitive mechanism, an improved calculation of crowding distance is adopted which substitutes traditional addition operation with multiplication operation, providing a more accurate depiction of population density. In addition, differential evolution (DE) is concatenated to diversify the population, and the key parameters of DE have been assigned different adjusting strategies to further enhance the overall performance. Proposed CMWOA is evaluated comprehensively on a series of benchmark functions with different shapes of true Pareto front. Results demonstrate that proposed CMWOA outperforms other three methods in most cases regarding several performance indicators. Particularly, influences of model parameters have also been discussed in detail. At last, proposed CMWOA is successfully applied to three real world problems, which further verifies the practicality of proposed algorithm.

Optimal harmonic mitigation strategy based on multiobjective whale optimization algorithm for asymmetrical half-cascaded multilevel inverter

One of the most significant issues related to the innovational multilevel inverters is the creation of high-step staircase quasi-sinusoidal voltage even with low number of semiconductors. In this paper, an innovational asymmetrical half-cascaded multilevel inverter (AHCMLI) is essentially proposed to solve this problem. The half-cascaded part of AHCMLI has operated as zero-positive level creator, and then, H-bridge part renders the perfect staircase quasi-sinusoidal voltage. The values of DC power sources of proposed AHCMLI have been defined based on an ingenious mathematical formula. On the other hand, due to the presence of high-order harmonics in output voltage of AHCMLI, they must be optimally mitigated. In this regard, an effectual and optimal rectangular notch–selective harmonic elimination–pulse width modulation (RN-SHEPWM) based on propagation proportion is suggested to simultaneously mitigate the twenty-ninth, thirty-first and thirty-fifth harmonics. Due to the multiobjective nature of the understudy problem and its special importance, it has been formulated based on multiobjective whale optimization algorithm (MOWOA). Finally, both the simulation and experimental results have decisively validated the level creation capability of proposed AHCMLI as compared to other structures. The values of twenty-ninth, thirty-first and thirty-fifth harmonics have been, respectively, mitigated to 0.0002857, 0.0003402 and 0.00029 using optimal RN-SHEPWM based on MOWOA.

Emergency Resource Allocation for Multi-Period Post-Disaster Using Multi-Objective Cellular Genetic Algorithm

As an important part of emergency response, the post-disaster emergency resource allocation is essential for mitigating disaster losses. To realize the effective allocation of relief materials and the reasonable selection of transportation routes, a multi-objective resource allocation model is proposed, considering the characteristics of uncertainty and persistence during rescue process. Furthermore, the multi-objective cellular genetic algorithm (MOCGA) is developed to solve the model by introducing the auxiliary population and neighborhood structure in the cellular automata. Finally, the comparison experiment proves that the overall performance of MOCGA is satisfactory compared with non-dominated multi-objective whale optimization algorithm (NSMOWOA), non-dominated multi-objective grey wolf optimizer (NSMOGWO) and non-dominated sorting genetic algorithm (NSGA-II) in the pareto front (PF), the hypervolume, the average value of objective function, and the PF ratio. Results show that MOCGA can solve the multi-objective dynamic emergency resource allocation model well, and can provide decision-makers with more excellent and diverse candidate rescue schemes than other algorithms. Besides, by analyzing the rescue schemes, this paper also provides a theoretical rescue scheme for decision-makers' scientific decisions.