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A modified whale optimization algorithm for large-scale global optimization problems

EXPERT SYSTEMS WITH APPLICATIONS 2018年 114卷 563-577页

作者： Sun, Yongjun Wang, Xilu Chen, Yahuan Liu, Zujun Xidian Univ Sch Telecommun Engn State Key Lab Integrated Serv Networks 2 South Taibai Rd Xian 710071 Shaanxi Peoples R China

As a new and competitive population-based optimization algorithm, the whale optimization algorithm (WOA) outperforms some other biological-inspired algorithms from the perspective of simplicity and efficiency. However, WOA will get stuck into local optima and degrade accuracy for large-scale global optimization (LSGO) problems. To address the issue, a modified whale optimization algorithm (MWOA) is proposed for solving LSGO problems. In order to balance the exploration and exploitation abilities, a nonlinear dynamic strategy based on a cosine function for updating the control parameter is given. A Levy-flight strategy is adopted to make the algorithm jump out of local optima. Moreover, a quadratic interpolation method is applied to the leader of the population, which enhances the local exploitation ability and improves the solution accuracy. MWOA is tested over 25 well-known benchmark functions with dimensions ranging from 100 to 1000. The experimental results demonstrate the superior performance of MWOA on LSGO, in terms of solution accuracy, convergence speed, and stability compared with other state-of-the-art optimization algorithms. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Large-scale global optimization whale optimization algorithm Levy flight Self-adaptive strategy Quadratic interpolation

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A whale optimization algorithm based on quadratic interpolation for high-dimensional global optimization problems

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APPLIED SOFT COMPUTING 2019年 85卷 105744-000页

作者： Sun, Yongjun Yang, Tong Liu, Zujun Xidian Univ State Key Lab Integrated Serv Network 2 South Taibai Rd Xian 710071 Shaanxi Peoples R China

whale optimization algorithm (WOA), as a new population-based optimization algorithm, performs well in solving optimization problems. However, when tackling high-dimensional global optimization problems, WOA tends to fall into local optimal solutions and has slow convergence rate and low solution accuracy. To address these problems, a whale optimization algorithm based on quadratic interpolation (QIWOA) is presented. On the one hand, a modified exploration process by introducing a new parameter is proposed to efficiently search the regions and deal with the premature convergence problem. On the other hand, quadratic interpolation around the best search agent helps QIWOA to improve the exploitation ability and the solution accuracy. Moreover, the algorithm tries to make a balance between exploitation and exploration. QIWOA is compared with several state-of-the-art algorithms on 30 high-dimensional benchmark functions with dimensions ranging from 100 to 2000. The experimental results show that QIWOA has faster convergence rate and higher solution accuracy than both WOA and other population-based algorithms. For functions with a flat or sharp bottom, QIWOA is difficult to find the global optimum, but it still performs best compared with other algorithms. (C) 2019 Elsevier B.V. All rights reserved.

关键词： High-dimensional optimization Quadratic interpolation whale optimization algorithm

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A Novel whale optimization algorithm for Cryptanalysis in Merkle-Hellman Cryptosystem

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MOBILE NETWORKS & APPLICATIONS 2018年第4期23卷 723-733页

作者： Abdel-Basset, Mohamed El-Shahat, Doaa El-henawy, Ibrahim Sangaiah, Arun Kumar Ahmed, Syed Hassan Zagazig Univ Fac Comp & Informat Dept Operat Res Zagazig Egypt Zagazig Univ Fac Comp & Informat Comp Sci Dept El Zera Sq Zagazig Sharqiyah Egypt Vellore Inst Technol Sch Comp Sci & Engn Vellore 632014 Tamil Nadu India Univ Cent Florida Dept Elect & Comp Engn Orlando FL 32816 USA

With the advance of the communication technology and the massive flow of information across the internet, it is becoming urgent to keep the confidentiality of the transmitted information. Using the internet has been extended to several fields such as e-mail, e-commerce, e-learning, health and medicine, shopping, and so on. Cryptography is the study of different techniques for securing the communication between the sender and the receiver. One of the most known cryptosystems is Merkle-Hellman Knapsack Cryptosystem (MHKC). It is one of the earliest Public Key Cryptosystem (PKC) that is used to secure the messages between the sender and the receiver. Developing a powerful cryptosystem comes after studying the fragility points of the current cryptosystems. The whale optimization algorithm (WOA) is one of the most recent nature-inspired meta-heuristic optimization algorithms, which simulates the social behavior of humpback whales. WOA has validated excellent performance in solving the continuous problems and the engineering optimization problems. This paper introduces a novel Modified version of WOA (MWOA) for cryptanalysis of MHKC. The sigmoid function is used to map the continuous values into discrete one. A penalty function is added to the evaluation function to deal with the infeasible solutions. The mutation operation is employed for improving the solutions. The results show that MWOA is more effective and robust than other algorithms in the literature.

关键词： whale optimization algorithm Knapsack cipher Subset sum problem Public key cryptosystem Merkle-Hellman Cryptosystem Cryptanalysis

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Parameter extraction of solar photovoltaic models by means of a hybrid differential evolution with whale optimization algorithm

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SOLAR ENERGY 2018年 176卷 742-761页

作者： Xiong, Guojiang Zhang, Jing Yuan, Xufeng Shi, Dongyuan He, Yu Yao, Gang Guizhou Univ Coll Elect Engn Guizhou Key Lab Intelligent Technol Power Syst Guiyang 550025 Guizhou Peoples R China Huazhong Univ Sci & Technol State Key Lab Adv Electromagnet Engn & Technol Wuhan 430074 Hubei Peoples R China Guizhou Elect Power Grid Dispatching & Control Ct Guiyang 550002 Guizhou Peoples R China

Parameter extraction of solar photovoltaic (PV) models is a typical complex nonlinear multivariable strongly coupled optimization problem. The original differential evolution (DE) is good at exploring the search space and locating the region of global optimum, but it is slow at exploitation of the solutions. Quite the opposite, the original whale optimization algorithm (WOA) is good at exploiting the population information, but it easily suffers from premature convergence. In such a context, in this paper, an effective hybrid method named DE/WOA by combining the exploration of DE with the exploitation of WOA is proposed for extracting the accurate parameters of PV models. A set of 13 numerical benchmark functions with different characteristics is firstly employed to verify the performance of DE/WOA. Then, DE/WOA is applied to parameter extraction of three PV models, i.e., single diode, double diode, and PV module models. Finally, DE/WOA is implemented to a practical PV power station in the Guizhou Power Grid of China to further validate its effectiveness under different irradiances, temperatures, and dynamic weather conditions. All the experimental results comprehensively demonstrate that DE/WOA performs significantly better than the original DE, WOA, and five advanced variants of them and is highly competitive with some recently-proposed parameter extraction methods.

关键词： Solar photovoltaic Parameter extraction Differential evolution whale optimization algorithm

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Synthesis of Broadside Linear Aperiodic Arrays With Sidelobe Suppression and Null Steering Using whale optimization algorithm

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IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS 2018年第2期17卷 347-350页

作者： Zhang, Chengyan Fu, Xiongjun Ligthart, Leo P. Peng, Shuilian Xie, Min Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Delft Univ Technol NL-2628 Delft Netherlands

In this letter, a new optimization algorithm named whale optimization algorithm (WOA) is introduced and applied to the synthesis of uniformly excited broadside linear aperiodic arrays with sidelobe suppression and null steering under constraints on beamwidth requirements. Three design examples of pattern synthesis have been chosen to illustrate the effectiveness and superiority of WOA when compared to synthesis techniques described in the literature. The results show that the WOA achieves lowest maximum sidelobe level combined with deepest null depth level with narrow first null beamwidth relative to other state-of-the-art algorithms. The WOA results and convergence tests demonstrate superior applicability of this novel optimization algorithm.

关键词： Broadside linear aperiodic arrays null steering sidelobe suppression whale optimization algorithm

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The Exploration/Exploitation Tradeoff in whale optimization algorithm

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IEEE ACCESS 2019年 7卷 125919-125928页

作者： Wu, Xiaoyang Zhang, Sen Xiao, Wendong Yin, Yixin Univ Sci & Technol Beijing Sch Automat & Elect Engn Minist Educ Key Lab Knowledge Automat Ind Proc Beijing 100083 Peoples R China

The whale optimization algorithm(WOA) is a novel meta-heuristic evolutionary algorithm inspired by the behavior of whales predation. An important factor to the success of WOA is the balancing between exploration and exploitation. In the WOA, the distance control parameter a is the main factor to find an appropriate balance between exploration and exploitation. In the standard WOA, the distance control parameter a is optimized by linear control strategy (LCS), but the process of whales predation is not simply linear process. To address the issue, this paper proposed a nonlinear control strategy based on arcsine function (NCS-Arcsin) to optimize WOA. The NCS-Arcsin is applied to adjust distance control parameter a. The NCS-Arcsin is considered to accurately describe the process of whales predation. Experiments on twelve well-known benchmark functions show the NCS-Arcsin can significantly improve the exploration and exploitation capabilities of WOA. In addition, the performance of proposed NCS-Arcsin is compared with LCS and other have been proposed NCS. The experimental results show that the optimization effect of NCS-Arcsin is stronger than that of LCS and other NCS. The NCSs based on the arcsine function is the best NCS, which can significantly improve the optimize performance of WOA.

关键词： whale optimization algorithm meta-heuristic algorithm nonlinear control strategy exploration and exploitation

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A novel methodology for optimal single mobile robot scheduling using whale optimization algorithm

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APPLIED SOFT COMPUTING 2019年 81卷 105520-000页

作者： Petrovic, Milica Miljkovic, Zoran Jokic, Aleksandar Univ Belgrade Fac Mech Engn Dept Prod Engn Lab Ind Robot & Artificial Intelligence Kraljice Marije 16 Belgrade 11120 35 Serbia

One of the fundamental requirements for creating an intelligent manufacturing environment is to develop a reliable, efficient and optimally scheduled material transport system. Besides traditional material transport solutions based on conveyor belts, industrial trucks, or automated guided vehicles, nowadays intelligent mobile robots are becoming widely used to satisfy this requirement. In this paper, the authors analyze a single mobile robot scheduling problem in order to find an optimal way to transport raw materials, goods, and parts within an intelligent manufacturing system. The proposed methodology is based on biologically inspired whale optimization algorithm (WOA) and is aimed to find the optimal solution of the nondeterministic polynomial-hard (NP-hard) scheduling problem. The authors propose a novel mathematical model for the problem and give a mathematical formulation for minimization of seven fitness functions (makespan, robot finishing time, transport time, balanced level of robot utilization, robot waiting time, job waiting time, as well as total robot and job waiting time). This newly developed methodology is extensively experimentally tested on 26 benchmark problems through three experimental studies and compared to five meta-heuristic algorithms including genetic algorithm (GA), simulated annealing (SA), generic and chaotic Particle Swarm optimization algorithm (PSO and cPSO), and hybrid GA-SA algorithm. Furthermore, the data are analyzed by using the Friedman statistical test to prove that results are statistically significant. Finally, generated scheduling plans are tested by Khepera II mobile robot within a laboratory model of the manufacturing environment. The experimental results show that the proposed methodology provides very competitive results compared to the state-of-art optimization algorithms. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Mobile robot Scheduling whale optimization algorithm Biologically inspired algorithms Intelligent manufacturing systems

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Optimal placement and sizing of battery energy storage system for losses reduction using whale optimization algorithm

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JOURNAL OF ENERGY STORAGE 2019年 26卷

作者： Wong, Ling Ai Ramachandaramurthy, Vigna K. Walker, Sara L. Taylor, Phil Sanjari, Mohammad Javad Univ Tenaga Nas Coll Engn Inst Power Engn Dept Elect Power Engn Jalan IKRAM UNITEN Kajang 43000 Selangor Malaysia Univ Coll Technol Sarawak Sch Engn & Technol Sibu 96000 Sarawak Malaysia Newcastle Univ Sch Engn Newcastle Upon Tyne NE1 7RU Tyne & Wear England Griffith Univ Sch Engn Gold Coast Qld 4222 Australia

This paper proposes an approach for optimal placement and sizing of battery energy storage system (BESS) to reduce the power losses in the distribution grid. A meta-heuristic optimization algorithm known as whale optimization algorithm (WOA) is introduced to perform the optimization. In this paper, two different approaches are presented to achieve the optimal allocation of the BESS. The first approach is to obtain the optimal location and sizing in two steps while the second approach optimizes both location and sizing simultaneously. The performance of the proposed technique has been validated by comparing with two other algorithms namely firefly algorithm and particle swarm optimization. The results show that WOA has outstanding performance in attaining the optimal location and sizing of BESS in the distribution network for power losses reduction.

关键词： Meta-heuristic optimization algorithm whale optimization algorithm BESS

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Multi-Objective Real Power Loss and Voltage Deviation Minimization for Grid Connected Micro Power System Using whale optimization algorithm

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INTERNATIONAL ENERGY JOURNAL 2018年第3期18卷 297-310页

作者： Ang, Sovann Leeton, Uthen Kulworawanichpong, Thanatchai Chayakulkeeree, Keerati Suranaree Univ Technol Sch Elect Engn Inst Engn Nakhon Ratchasima 30000 Thailand

In this paper, a whale optimization algorithm (WOA) is applied to solve the multi-objective real power loss and bus voltage deviation (VD) minimizations for grid connected micro power system with non firm small power plants. The control variables are the voltage magnitude at voltage control buses and the transformer tap changers. The methods were tested with IEEE 6 and 14 buses systems comparing to genetic algorithm (GA), artificial bee colony (ABC), and particle swarm optimization (PSO). The simulation results shown that WOA can successfully provide the minimum the multi-objective real power loss and bus VD solution than those computed by GA, ABC, and PSO. Moreover, the applied methods use the minimum computation time among all methods.

关键词： loss minimization optimal power flow voltage deviation whale optimization algorithm

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A Pareto-Based Hybrid whale optimization algorithm with Tabu Search for Multi-Objective optimization

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algorithmS 2019年第12期12卷 261页

作者： AbdelAziz, Amr Mohamed Soliman, Taysir Hassan A. Ghany, Kareem Kamal A. Sewisy, Adel Abu El-Magd Beni Suef Univ Fac Comp & Artificial Intelligence Bani Suwayf 62111 Egypt Assiut Univ Fac Comp & Informat Assiut 71516 Egypt Saudi Elect Univ Coll Comp & Informat Riyadh 11673 Saudi Arabia

Multi-Objective Problems (MOPs) are common real-life problems that can be found in different fields, such as bioinformatics and scheduling. Pareto optimization (PO) is a popular method for solving MOPs, which optimizes all objectives simultaneously. It provides an effective way to evaluate the quality of multi-objective solutions. Swarm Intelligence (SI) methods are population-based methods that generate multiple solutions to the problem, providing SI methods suitable for MOP solutions. SI methods have certain drawbacks when applied to MOPs, such as swarm leader selection and obtaining evenly distributed solutions over solution space. whale optimization algorithm (WOA) is a recent SI method. In this paper, we propose combining WOA with Tabu Search (TS) for MOPs (MOWOATS). MOWOATS uses TS to store non-dominated solutions in elite lists to guide swarm members, which overcomes the swarm leader selection problem. MOWOATS employs crossover in both intensification and diversification phases to improve diversity of the population. MOWOATS proposes a new diversification step to eliminate the need for local search methods. MOWOATS has been tested over different benchmark multi-objective test functions, such as CEC2009, ZDT, and DTLZ. Results present the efficiency of MOWOATS in finding solutions near Pareto front and evenly distributed over solution space.

关键词： Multi-Objective optimization Multi-Objective Problems Pareto optimization Swarm Intelligence Tabu Search whale optimization algorithm

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