Modern metaheuristic algorithms such as particle swarm optimization and cuckoo search start to demonstrate their power in dealing with tough optimization problems and even NP-hard problems. This book reviews and introduces the state-of-the-art nature-inspired metaheuristic algorithms for global optimization, including ant and bee algorithms, bat algorithm, cuckoo search, differential evolution, firefly algorithm, genetic algorithms, harmony search, particle swarm optimization, simulated annealing and support vector machines. In this revised edition, we also include how to deal with nonlinear constraints. Worked examples with implementation have been used to show how each algorithm works. This book is thus an ideal textbook for an undergraduate and/or graduate course as well as for self study. As some of the algorithms such as the cuckoo search and firefly algorithms are at the forefront of current research, this book can also serve as a reference for researchers.
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The data visualization system of the sea-crossing bridge is an intuitive observation and evolution analysis of the bridge and the seabed topography, landform, and strata at the top of the tunnel around the bridge, assisting maintenance personnel to carry out operations and providing support information for decision makers. Through the fusion of multi-source seabed topography data, providing two-dimensional, three-dimensional, and infographic visualization of these data is a key link in building a visualization system. This paper mainly introduces three types of submarine terrain detection technologies, namely multiple beams, side-scan sonar, and shallow stratigraphic profile, as well as the key algorithms in the process of data post-processing, and visualizes the surrounding terrain of the Hong Kong-Zhuhai-Macao Bridge through data fusion.
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This work addresses the coordination problem of multiple robots with the goal of finding specific hazardous targets in an unknown area and dealing with them cooperatively. The desired behavior for the robotic system entails multiple requirements, which may also be conflicting. The paper presents the problem as a constrained bi-objective optimization problem in which mobile robots must perform two specific tasks of exploration and at same time cooperation and coordination for disarming the hazardous targets. These objectives are opposed goals, in which one may be favored, but only at the expense of the other. Therefore, a good trade-off must be found. For this purpose, a nature-inspired approach and an analytical mathematical model to solve this problem considering a single equivalent weighted objective function are presented. The results of proposed coordination model, simulated in a two dimensional terrain, are showed in order to assess the behavior of the proposed solution to tackle this problem. We have analyzed the performance of the approach and the influence of the weights of the objective function under different conditions: static and dynamic. In this latter situation, the robots may fail under the stringent limited budget of energy or for hazardous events. The paper concludes with a critical discussion of the experimental results.
Hankel matrix systems often arise in many problems of signal analysis. Toeplitz matrix systems is a special case of a Hankel matrix equations. Both the auto-correlation and the covariance matrix equations are different forms of the Hankel matrix equations. Trench has developed a direct method that can solve Hankel matrix equations in θ(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) operations. In this paper, we propose an alternate algorithm. This new method is a combination of the FFT and the conjugate gradient method. The advantage of this new approach is that it is computationally robust to highly ill-conditioned and even singular matrix equations. Preliminary results indicated that for very large complex Toeplitz matrix equations, the CPU time is proportional to N as the number of unknowns as increased, as opposed to N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> for conventional methods.
The pharmaceutical powder and tableting process is simulated using a combined finite-discrete element method and contact dynamics for irregular-shaped particles. The particle-scale formulation and two-stage contact detection algorithm which has been developed for the proposed method enhances the overall calculation efficiency for particle interaction characteristics. The irregular particle shapes and random sizes are represented as a pseudo-particle assembly having a scaled up geometry but based on the variations of real powder particles. Our simulations show that particle size, shapes and material properties have a significant influence on the behaviour of compaction and deformation. Copyright © 2005 John Wiley & Sons, Ltd.
Design problems in industrial engineering often involve a large number of design variables with multiple objectives, under complex nonlinear constraints. The algorithms for multiobjective problems can be significantly different from the methods for single objective optimization. To find the Pareto front and non-dominated set for a nonlinear multiobjective optimization problem may require significant computing effort, even for seemingly simple problems. Metaheuristic algorithms start to show their advantages in dealing with multiobjective optimization. In this paper, we extend the recently developed firefly algorithm to solve multiobjective optimization problems. We validate the proposed approach using a selected subset of test functions and then apply it to solve design optimization benchmarks. We will discuss our results and provide topics for further research.
Key update and residence management have been investigated as an effective solution to cope with desynchronization attacks in mobility management entity (MME) handovers. In this paper, we first analyze the impacts of the key update interval (KUI) and MME residence interval (MRI) on handover processes and their secrecy performance in terms of the number of exposed packets (NEP), signaling overhead rate (SOR), and outage probability of vulnerability (OPV). Specifically, the bounds of the derived NEP and SOR not only capture their behaviors at the boundary of the KUI and MRI, but also show the tradeoff between the NEP and SOR. Additionally, through the analysis of the OPV, it is shown that the handover security can be enhanced by shortening the KUI and the desynchronization attacks can be avoided with high-mobility users. The above facts accordingly motivate us to propose a multi-objective optimization (MO) problem to find the optimal KUI and MRI that minimize both the NEP and SOR subject to the constraint on the OPV. To this end, two scalarization techniques are adapted to transform the proposed MO problem into single-objective optimization problems, i.e., an achievement-function method via fractional programming (FP) and a weighted-sum method. Based on the derived bounds on NEP and SOR, the FP approach can be optimally solved via a simple numerical method. For the weighted-sum method, the firefly algorithm (FA) is utilized to find the optimal solution. The results show that both techniques can solve the proposed MO problem with a significantly reduced searching complexity compared to the conventional heuristic iterative search technique.
Constrained multi-objective optimization problems exist widely in real-world applications, and they involve a simultaneous optimization of multiple and often conflicting objectives subject to several equality and/or inequality constraints. To deal with these problems, a crucial issue is how to handle constraints effectively. This paper proposes a simple yet effective constrained decomposition-based multi-objective evolutionary algorithm. In the proposal, the evolutionary process is divided into two stages in which constraints are handled differently. In the first stage, constraints are totally ignored and the population is pulled toward the unconstrained Pareto-optimal front (PF) by optimizing objectives only. This can help the proposed algorithm handle well problems with the following features, i.e., the constrained PF has an intersection with the unconstrained counterpart, and there are infeasible regions blocking the way of convergence. In the second stage, with the purpose of approximating the constrained PF well,constraint satisfaction is emphasized over objective minimization.Moreover, different evolutionary frameworks are adopted in the two stages to promote the performance of the algorithm as much as possible. The proposed algorithm is comprehensively compared with several state-of-the-art algorithms on 39 problems (with 266 test instances in total), including one real-world problem (with 36 instances) in search-based software engineering. As shown by the experimental results, the new algorithm performs best on the majority of these problems, particularly on those with the aforementioned features. In summary, the suggested algorithm provides an effective way of handling constrained multi-objective optimization problems.
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Pulsating heat pipes (PHP) can be an effcient solution to heat transfer problems in many applications. However, mathematical modelling of a PHP system still has many challenging issues due to the complexity and multiphysics nature of the system. In this paper, we use a simplified mathematical model which can still capture most of the known physics of the two-phase heat transfer system. With appropriate approximations, we can analyze the short-time behaviour of the model and make predictions concerning the startup characteristics. We also solve the full mathematical model using finite difference methods and compare the results with available experimental data. Comparison and simulations show that this model can produce many important features of a real system. Potential improvements and further research topics are also discussed.
In the last few years, metaheuristic-driven optimization has been employed to address deep belief network (DBN) model selection, since it provides simple and elegant solutions in a wide range of applications. In this work, we introduce the well-known cuckoo search to fine-tune DBN parameters and validate its effectiveness by comparing it with harmony search, improved harmony search, and particle swarm optimization. The experimental results have been carried out in two public datasets using DBNs with a different number of layers concerning the task of binary image reconstruction.
