A new hybrid model for wind speed forecasting combining long short-term memory neural network, decomposition methods and grey wolf optimizer

Efficient boosted grey wolf optimizers for global search and kernel extreme learning machine training

Disasters have caused significant losses to humans in the past decades. It is essential to learn about the disaster situation so that rescue works can be conducted as soon as possible. Unmanned aerial vehicle (UAV) is a very useful and effective tool to improve the capacity of disaster situational awareness for responders. In the paper, UAV path planning is modelled as the optimization problem, in which fitness functions include travelling distance and risk of UAV, three constraints involve the height of UAV, angle of UAV, and limited UAV slope. An adaptive selection mutation constrained differential evolution algorithm is put forward to solve the problem. In the proposed algorithm, individuals are selected depending on their fitness values and constraint violations. The better the individual is, the higher the chosen probability it has. These selected individuals are used to make mutation, and the algorithm searches around the best individual among the selected individuals. The well-designed mechanism improves the exploitation and maintains the exploration. The experimental results have indicated that the proposed algorithm is competitive compared with the state-of-art algorithms, which makes it more suitable in the disaster scenario.

A constrained differential evolution algorithm to solve UAV path planning in disaster scenarios

The bio-inspired algorithms are novel, modern, and efficient tools for the design of electrical machines. However, from the mathematical point of view, these problems belong to the most general branch of non-linear optimization problems, where these tools cannot guarantee that a global minimum is found. The numerical cost and the accuracy of these algorithms depend on the initialization of their internal parameters, which may themselves be the subject of parameter tuning according to the application. In practice, these optimization problems are even more challenging, because engineers are looking for robust designs, which are not sensitive to the tolerances and the manufacturing uncertainties. These criteria further increase these computationally expensive problems due to the additional evaluations of the goal function. The goal of this paper is to give an overview of the widely used optimization techniques in electrical machinery and to summarize the challenges and open problems in the applications of the robust design optimization and the prospects in the case of the newly emerging technologies.

Robust Design Optimization and Emerging Technologies for Electrical Machines: Challenges and Open Problems

In this paper, a novel swarm-based metaheuristic algorithm is proposed, which is called tuna swarm optimization (TSO). The main inspiration for TSO is based on the cooperative foraging behavior of tuna swarm. The work mimics two foraging behaviors of tuna swarm, including spiral foraging and parabolic foraging, for developing an effective metaheuristic algorithm. The performance of TSO is evaluated by comparison with other metaheuristics on a set of benchmark functions and several real engineering problems. Sensitivity, scalability, robustness, and convergence analyses were used and combined with the Wilcoxon rank-sum test and Friedman test. The simulation results show that TSO performs better compared to other comparative algorithms.

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Tuna Swarm Optimization: A Novel Swarm-Based Metaheuristic Algorithm for Global Optimization

A grey wolf optimizer using Gaussian estimation of distribution and its application in the multi-UAV multi-target urban tracking problem

To overcome the premature convergence caused by the ill-distribution of solutions in the basic Gaussian estimation of distribution algorithm (GEDA), this paper explores a novel GEDA variant with random walk strategies, namely RW-GEDA. In RW-GEDA, the weighted maximum likelihood estimation method is used to estimate the Gaussian distribution. The new candidates are sampled using a shifted mean to enhance exploration performance. When the algorithm stagnates, two random walk strategies, namely, Gaussian random walk and Levy walk, are activated to enrich the population diversity. Moreover, RW-GEDA is executed in an Eigen coordinate framework to promote the evolution towards the dominant region. The performance of RW-GEDA is evaluated by using the CEC 2014 test suite and compared with other top algorithms from different communities as well as promising GEDA extensions. The statistical results demonstrate the competitive performance of our proposed RW-GEDA in terms of efficiency and accuracy. In addition, RW-GEDA is applied to solve the optimal missile guidance handover problem. To fill the gap in solving this problem, a novel missile guidance advantage model is established, and the optimal missile guidance handover is determined by optimizing the control variables of unmanned combat aerial vehicles. The validity and practicability of the problem model as well as the accuracy and efficiency of RW-GEDA are demonstrated by the experimental results.

A Gaussian Estimation of Distribution Algorithm With Random Walk Strategies and Its Application in Optimal Missile Guidance Handover for Multi-UCAV in Over-the-Horizon Air Combat

A covariance matrix adaptation evolution strategy variant and its engineering application

To overcome the defect of whale optimization algorithm (WOA) being easily fallen into local optimum caused by the ill-distribution of solutions, this paper explores an adaptive WOA variant using Gaussian distribution strategies (GDSs), named GDS-WOA. In GDS-WOA, by means of one GDS, named the Gaussian estimation of distribution method, the superior population information is used to evolve the distribution scope and modify the evolution direction. Moreover, an adaptive framework is adopted to integrate the Gaussian estimation of distribution method and WOA together, in which each individual can update its position using Gaussian estimation of distribution method or WOA according to an adaptive probability parameter. When the algorithm falls into stagnation, another GDS, named Gaussian random walk, is activated to enrich the population diversity and help the algorithm get rid of the local optimum. Additionally, the greedy strategy is carried out to select the offspring from the parents and the generated candidates to fully retain the promising solutions. The GDS-WOA is benchmarked on CEC 2014 test suite, and the performance of GDS-WOA is evaluated by comparing with WOA and its promising variant IWOA, as well as other five state-of-the-art evolutionary algorithms, i.e., COA, VCS, CoBiDE, HFPSO and GWO. The statistical results demonstrate that GDS-WOA outperforms other competitors in terms of convergence efficiency and accuracy. Finally, GDS-WOA is applied to solve the optimal task allocation problem of heterogeneous unmanned combat aerial vehicles (UCAVs). To address this constrained real-world optimizing problem efficiently, the mathematical model of heterogeneous UCAVs task allocation is described with the operational effectiveness value as the objective. The validity and practicauility of the model as well as the performance of GDS-WOA for solving constrained optimization problem are demonstrated by the experimental results.

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An Adaptive Whale Optimization Algorithm Using Gaussian Distribution Strategies and Its Application in Heterogeneous UCAVs Task Allocation

The estimation of distribution algorithm (EDA) is a well-known stochastic search method but is easily affected by the ill-shaped distribution of solutions and can thus become trapped in stagnation. In this paper, we propose a novel modified EDA with a multi-leader search (MLS) mechanism, namely, the MLS-EDA. To strengthen the exploration performance, an enhanced distribution model that considers the information of population and distribution is utilized to generate new candidates. Moreover, when the algorithm stagnates, the MLS mechanism will be activated to perform a local search and shrink the search scope. The performance of the MLS-EDA in addressing complex optimization problems is verified using the CEC 2014 and CEC 2017 testbeds with 30D, 50D and 100D tests. Several modern algorithms, including the top-performing methods in the CEC 2014 and CEC 2017 competitions, are considered as competitors. The competitive performance of our proposed MLS-EDA is discussed based on the comparison results.

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Hui Zhao

Papers

A grey wolf optimizer using Gaussian estimation of distribution and its application in the multi-UAV multi-target urban tracking problem

A Gaussian Estimation of Distribution Algorithm With Random Walk Strategies and Its Application in Optimal Missile Guidance Handover for Multi-UCAV in Over-the-Horizon Air Combat

A covariance matrix adaptation evolution strategy variant and its engineering application

An Adaptive Whale Optimization Algorithm Using Gaussian Distribution Strategies and Its Application in Heterogeneous UCAVs Task Allocation

An Estimation of Distribution Algorithm With Multi-Leader Search