Multi-objective Whale Optimization Research Articles

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.

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.

Distributed clustering in peer to peer networks using multi-objective whale optimization

Multi-objective clustering algorithms have superiority over its single objective counterparts as they include additional knowledge on properties of data in the form of objectives to discover the underlying clusters present in various datasets. This paper proposes a distributed clustering algorithm using multi-objective whale optimization (DMOWOA) for peer to peer network. The algorithm minimizes two objective functions to perform clustering, namely : Total Euclidean Deviation and Total Symmetrical Deviation. Both objective values are shared using diffusion method of cooperation to obtain correct partitioning at each peer. A single solution from the non-dominated solutions is selected as final solution based on its minimum distance to origin in the normalized objective space. The proposed algorithm’s performance is evaluated on four synthetic and five real-life wireless sensor network datasets (Canada weather station dataset, Delhi air pollution content dataset, Intel laboratory dataset, Washington cook agronomy farm dataset and Thames river water quality dataset). The comparison is carried out with multi-objective distributed particle swarm optimization (DMOPSO), distributed K-Means (DK-Means) and other seven recently developed nature inspired multi-objective clustering techniques. The proposed algorithm in most of the cases outperforms the existing techniques in terms of statistical measures Minkowski Score, Dunn index and Silhouette index. The average rank of the proposed algorithm is also better in Kruskal–Wallis statistical test.

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.

Multi-Objective Whale Optimization Algorithm for Balance Recovery of a Humanoid Robot

Multi-Objective Whale Optimization Algorithm for Balance Recovery of a Humanoid Robot

Multi-Objective Whale Optimization Algorithm of Humanoid Robot Walking and Carry Objects on Inclined Planes

Multi-Objective Whale Optimization Algorithm of Humanoid Robot Walking and Carry Objects on Inclined Planes

Encryption-based steganography of images by multiobjective whale optimal pixel selection

ABSTRACT Information security involves hiding secret images, text, audio or video files within any other files that can be used to preserve the secret data from being theft by the third party. Recent works of the literature in steganography have the threat of secret information being retrieved by an anonymous user. Some techniques do not support some image formats for embedding. To overcome the disadvantages associated with the existing algorithms, we proposed encryption-based steganography. Encryption algorithms play vital roles to protect original data from unauthorized access. In this paper, the cover image is separated into RGB components separately. Multilevel discrete wavelet transform (DWT) transformation is applied over the transformed image components. Optimal pixels are selected using Multiobjective Whale Optimization. The secret image is split into RGB components. Encryption using Data Encryption Standard, Advanced Encryption Standard and Signcryption algorithms is performed over R, G and B separated components, respectively. The encrypted image is embedded into the selected pixel point of cover image. Inverse DWT is applied to retrieve the stego image. The results are analyzed to prove low error rate with high secured secret image.

Multi-objective whale optimization based minimization of loss, maximization of voltage stability considering cost of DG for optimal sizing and placement of DG

Huge need in electricity causes placement of Distribution Generation (DG)s like Photovoltaics (PV) systems in distribution side for enhancing the loadability by improving the voltage stability and minimization of loss with minimum cost. Many optimal placements of DG have done in focus of minimum loss and improving voltage profile. This Whale optimization is a new optimization technique framed with mathematics of spiral bubble-net feeding behavior of humpback whales for solving a power system multi-objective problem considering cost of the power tariff and DG. Here main objectives are minimizing loss and cost with maximization of voltage stability index. IEEE 69 power system data is used for solution of the proposed method.

Maximizing lifetime of large-scale wireless sensor networks using multi-objective whale optimization algorithm

The sink nodes in large-scale wireless sensor networks (LSWSNs) are responsible for receiving and processing the collected data from sensor nodes. Identifying the locations of sink nodes in LSWSNs play a vital role in term of saving energy. Furthermore, sink nodes have extremely extra resources such as large memory, powerful batteries, long-range antenna, etc. This paper proposes a multi-objective whale optimization algorithm (MOWOA) to determine the lowest number of sink nodes that cover the whole network. The major aim of MOWOA is to reduce the energy consumption and prolongs the lifetime of LSWSNs. To achieve these objectives, a fitness function has been formulated to decrease energy consumption and maximize the network’s lifetime. The experimental results revealed that the proposed MOWOA achieved a better efficiency in reducing the total power consumption by 26% compared with four well-known optimization algorithms: multi-objective grasshopper optimization algorithm, multi-objective salp swarm algorithm, multi-objective gray wolf optimization, multi-objective particle swarm optimization over all networks sizes.

Opposition-based multi-objective whale optimization algorithm with global grid ranking

Nature-inspired computing has attracted a lot of research effort especially for addressing real-world multi-objective optimization problem (MOP). This paper proposes a new nature-inspired optimization algorithm which is named opposition-based multi-objective whale optimization algorithm with global grid ranking (MOWOA). The proposed approach utilizes several parts to enhance the performance in optimization. First, the efficient evolution process is inherited from the single objective whale optimization algorithm(WOA). Second, opposition-based learning(OBL) is applied into the algorithm. Meanwhile, a novel mechanism called global grid ranking(GGR) which is inspired by grid mechanism has been incorporated into the proposed algorithm. To show the significance of the proposed algorithm, MOWOA is tested on a diverse set of benchmark with a series of well-known evolutionary algorithms and the influence of each individual strategy is also verified through 14 benchmarks. Moreover, the new proposed algorithm is also applied to the simple data clustering problem and a real-world water optimization problem in China. The results demonstrate that MOWOA is not only an algorithm with well performance for bench-mark problems but also expected to have a more wide application in real-world engineering problems.

Optimization for Hydro-Photovoltaic-Wind Power Generation System Based on Modified Version of Multi-Objective Whale Optimization Algorithm

The intractable problem of the solar energy and wind energy are their discontinuity and instability cause by the environmental change. Hydropower was often chosen as the compensation of electric energy system for its celerity and low cost of adjustment and respond. This paper presents a long term multi-objective optimization model of hydro-photovoltaic(PV)-wind power system, in which, cascade hydropower station acts as the compensation of the power system. One objective of the model is maximizing the annual total power generation of the power system, and another objective is to smoothen the fluctuation of the power output of the system. This model calculates all the PV power and wind power at first, and then inputs the calculated results to the power grid as the boundary condition of the hydropower optimization. A modified version of non-dominated sorting whale optimization algorithm (modified NSWOA) is proposed to get a solution set of the proposed model. The results demonstrate that the modified NSWOA can provide decision maker a series of solutions for optimal selection and the hydropower can well compensate the PV power and wind power by its great adjusting ability.

Multi-Step-Ahead Carbon Price Forecasting Based on Variational Mode Decomposition and Fast Multi-Output Relevance Vector Regression Optimized by the Multi-Objective Whale Optimization Algorithm

The accurate and stable forecasting of carbon prices is vital for governors to make policies and essential for market participants to make investment decisions, which is important for promoting the development of carbon markets and reducing carbon emissions in China. However, it is challenging to improve the carbon price forecasting accuracy due to its non-linearity and non-stationary characteristics, especially in multi-step-ahead forecasting. In this paper, a hybrid multi-step-ahead forecasting model based on variational mode decomposition (VMD), fast multi-output relevance vector regression (FMRVR), and the multi-objective whale optimization algorithm (MOWOA) is proposed. VMD is employed to extract the primary mode for the carbon price. Then, FMRVR, which is used as the forecasting module, is built on the preprocessed data. To achieve high accuracy and stability, the MOWOA is utilized to optimize the kernel parameter and input the lag of the FMRVR. The proposed hybrid forecasting model is applied to carbon price series from three major regional carbon emission exchanges in China. Results show that the proposed VMD-FMRVR-MOWOA model achieves better performance compared to several other multi-output models in terms of forecasting accuracy and stability. The proposed model can be a potential and effective technique for multi-step-ahead carbon price forecasting in China’s three major regional emission exchanges.

Multi-objective whale optimization algorithm for content-based image retrieval

In the recent years, there are massive digital images collections in many fields of our life, which led the technology to find methods to search and retrieve these images efficiently. The content-based is one of the popular methods used to retrieve images, which depends on the color, texture and shape descriptors to extract features from images. However, the performance of the content-based image retrieval methods depends on the size of features that are extracted from images and the classification accuracy. Therefore, this problem is considered as a multi-objective and there are several methods that used to manipulate it such as NSGA-II and NSMOPSO. However, these methods have drawbacks such as their time and space complexity are large since they used traditional non-dominated sorting methods. In this paper, a new non-dominated sorting based on multi-objective whale optimization algorithm is proposed for content-based image retrieval (NSMOWOA). The proposed method avoids the drawbacks in other non-dominated sorting multi-objective methods that have been used for content-based image retrieval through reducing the space and time complexity. The results of the NSMOWOA showed a good performance in content-based image retrieval problem in terms of recall and precision.

A novel hybrid system based on a new proposed algorithm—Multi-Objective Whale Optimization Algorithm for wind speed forecasting

In recent years, managers and researchers have paid increasing attention to accurate and stable wind speed prediction due to its significant effect on power dispatching and power grid security. However, most previous research has focused only on enhancing either accuracy or stability, with few studies addressing the two issues, simultaneously. This task is challenging due to the intermittency and complex fluctuations of wind speed. Therefore, we proposed a novel hybrid system based on a newly proposed called the MOWOA, which includes four modules: a data preprocessing module, optimization module, forecasting module, and evaluation module. An effective decomposing technique is also applied to eliminate redundant noise and extract the primary characteristics of wind speed data. In order to obtain high accuracy, and stability for wind speed prediction simultaneously, and overcome the weaknesses of single objective optimization algorithms, the optimization module of the proposed MOWOA is utilized to optimize the weights and thresholds of the Elman neutral network used in the forecasting module. Finally, the evaluation module, which includes hypothesis testing, evaluation criteria, and three experiments, is introduced perform comprehensive evaluation on the system. The results indicate that the proposed MOWOA performs better than the two recently developed MOALO and MODA algorithms, and that the proposed hybrid model outperforms all sixteen models used for comparison, which demonstrates its superior ability to generate forecasts in terms of forecasting accuracy and stability.