15 papers · ranked by Valyu relevance
Hardi M. Mohammed, Shahla U. Umar, Tarik A. Rashid
The whale optimization algorithm (WOA) is a nature-inspired metaheuristic optimization algorithm, which was proposed by Mirjalili and Lewis in 2016. This algorithm has shown its ability to solve many problems. Comprehensive surveys have been conducted about some other nature-inspired algorithms, such as ABC and PSO.…
Gaige Wang, Lihong Guo, Hong Duan, Luo Liu + 1 more
Path planning for uninhabited combat air vehicle (UCAV) is a complicated high dimension optimization problem, which mainly centralizes on optimizing the flight route considering the different kinds of constrains under complicated battle field environments. Original bat algorithm (BA) is used to solve the UCAV path…
Sha-Sha Guo, Jie-Sheng Wang, Xiao-Xu Ma
The bat algorithm (BA) is a heuristic algorithm that globally optimizes by simulating the bat echolocation behavior. In order to improve the search performance and further improve the convergence speed and optimization precision of the bat algorithm, an improved algorithm based on chaotic map is introduced, and the…
Nafrizuan Mat Yahya, M. Osman Tokhi, Hyreil Anuar Kasdirin
Bats sonar algorithm (BSA) as a swarm intelligence approach utilises the concept of echolocation of bats to find prey. However, the algorithm is unable to achieve good precision and fast convergence rate to the optimum solution. With this in mind, an adaptive bats sonar algorithm is introduced with new paradigms of…
Jian Xie, Yongquan Zhou, Huan Chen
Aiming at the phenomenon of slow convergence rate and low accuracy of bat algorithm, a novel bat algorithm based on differential operator and Lévy flights trajectory is proposed. In this paper, a differential operator is introduced to accelerate the convergence speed of proposed algorithm, which is similar to mutation…
Binglian Zhu, Wenyong Zhu, Zijuan Liu, Qingyan Duan + 1 more
This paper proposes a novel quantum-behaved bat algorithm with the direction of mean best position (QMBA). In QMBA, the position of each bat is mainly updated by the current optimal solution in the early stage of searching and in the late search it also depends on the mean best position which can enhance the…
Sichen Pan, Tarun Kumar Gupta, Khalid Raza, Bilal Alatas
Deep feedforward neural networks (DFNNs) have attained remarkable success in almost every computational task. However, the selection of DFNN architecture is still based on handcraft or hit-and-trial methods. Therefore, an essential factor regarding DFNN is about designing its architecture. Unfortunately, creating…
Jaemin Kim, Younghwan Yoo
This paper proposes a sensor node activation method using the nature-inspired algorithm (NIA) for the target coverage problem. The NIAs have been used to solve various optimization problems. This paper formulates the sensor target coverage problem into an object function and solves it with an NIA, specifically, the bat…
Simon Fong, Suash Deb, Xin-She Yang, Yan Zhuang
Traditional K-means clustering algorithms have the drawback of getting stuck at local optima that depend on the random values of initial centroids. Optimization algorithms have their advantages in guiding iterative computation to search for global optima while avoiding local optima. The algorithms help speed up the…
Huanyu Dong, Xiaohui Yang, Anyi Li, Zihao Xie + 1 more
Prognostics and Health Management (PHM) is an emerging technique which can improve the availability and efficiency of equipment. A series of related optimization of the PHM system has been achieved due to the growing need for lowering the cost of maintenance. The PHM system highly relies on data collected from its…
Waqar Younas, Gauhar Ali, Naveed Ahmad, Qamar Abbas + 6 more
'Muhammad Talha Masood' 'Asim Munir' 'Mohammed ElAffendi' 'Shah Nazir' 'Habib Ullah Khan' 'Iván García-Magariño'] Metaheuristic algorithms are effectively used in searching some optical solution space. for optical solution. It is basically the type of local search generalization that can provide useful solutions for…
Yongquan Zhou, Jian Xie, Liangliang Li, Mingzhi Ma
Bat algorithm (BA) is a novel stochastic global optimization algorithm. Cloud model is an effective tool in transforming between qualitative concepts and their quantitative representation. Based on the bat echolocation mechanism and excellent characteristics of cloud model on uncertainty knowledge representation, a new…
Uzman Perwaiz, Irfan Younas, Adeem Ali Anwar, Sergio Gómez
In many objective optimization problems (MaOPs), more than three distinct objectives are optimized. The challenging part in MaOPs is to get the Pareto approximation (PA) with high diversity and good convergence. In Literature, in order to solve the issue of diversity and convergence in MaOPs, many approaches are…
Ahmed Majid Taha, Aida Mustapha, Soong-Der Chen
When the amount of data and information is said to double in every 20 months or so, feature selection has become highly important and beneficial. Further improvements in feature selection will positively affect a wide array of applications in fields such as pattern recognition, machine learning, or signal processing.…
Fatima Nazari Robati, Hossein Akbarifard, Seyyed abdolmajid Jalaee
Collective intelligence is one of the most powerful optimization techniques based on group behaviors of organisms. Bat algorithm (BA) is an algorithm inspired by the collective action of bats in the wild, presented in 2010 by Yang. Researchers have made several efforts to improve these algorithms. This article…