optimization problems constrained by partial differential equations (PDEs) naturally arise in scientific computing, as those constraints often model physical systems or the simulation thereof. In an implicitly constra...
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Pokemon GO is one of the most popular Pokemon games. This game consists of walking around the world and collecting Pokemon characters using augmented reality. In addition, you can battle with friends, join a gym, or m...
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ISBN:
(纸本)9781728184326
Pokemon GO is one of the most popular Pokemon games. This game consists of walking around the world and collecting Pokemon characters using augmented reality. In addition, you can battle with friends, join a gym, or make attacks. These battles must happen between teams with the same size, and this poses a question that is related to the best combination for a team to beat a given opposing team. In order to solve this problem, one can use optimization algorithms. In this paper, we investigate three optimization algorithms to solve this problem: genetic algorithm (GA), memetic algorithm (MA), and iterated local search (ILS). In our experiments, we use time and fitness as evaluation metrics. Our findings indicate that the fastest algorithm is ILS with an execution time of 1.49 +/- 0.11 seconds, followed by GA with an execution time of 1.51 +/- 0.10 seconds, and MA with an execution time of 13.41 +/- 1.00 seconds. However, when we consider the fitness metric, MA achieves the best average fitness of 50, 366.27 +/- 12, 055.53, followed by GA, 43, 113.00 +/- 10, 482.30, and ILS, 31, 224.32 +/- 7, 943.70. All these results are statistically significant to the others according to the post-hoc Friedman test. Analyzing all the obtained results, we recommend the use of the ILS algorithm when the execution time is of utmost importance. However, if fitness is important, then we recommend the use of the memetic algorithm. Finally, if both the execution time and fitness are deemed equally important, then, we recommend the usage of the genetic algorithm because it has a runtime similar to ILS and reasonable fitness.
System reliability optimization problems have been widely discussed to maximize system reliability with resource *** importance is a wellknown method for evaluating the effect of component reliability on system *** im...
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System reliability optimization problems have been widely discussed to maximize system reliability with resource *** importance is a wellknown method for evaluating the effect of component reliability on system *** importance measures(IMs)are extended for binary,multistate,and continuous systems from different aspects based on the Bimbaum ***,these IMs have been applied in allocating limited resources to the component to maximize system ***,the significance of Bimbaum importance is illustrated from the perspective of probability principle and gradient geometrical ***,the equations of various extended IMs are provided *** rules for simple optimization problems are summarized to enhance system reliability by using ranking or heuristic methods based on *** importance-based optimization algorithms for complex or large-scale systems are generalized to obtain remarkable solutions by using IM-based local search or simplification ***,a general framework driven by IM is developed to solve optimization ***,some challenges in system reliability optimization that need to be solved in the future are presented.
Sine Cosine Algorithm (SCA) was recognized as a lightweight, efficient, and has a clear math principal optimizer. However, SCA still suffers from a set of problems such as stagnation at local optima, a slow convergenc...
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Sine Cosine Algorithm (SCA) was recognized as a lightweight, efficient, and has a clear math principal optimizer. However, SCA still suffers from a set of problems such as stagnation at local optima, a slow convergence curve, and a lack of efficient balancing between exploration and exploitation search modes. To mitigate these limitations and improve SCA performance, this study introduces a new version of SCA called QLESCA that smartly controls SCA parameters through an embedded Q-learning algorithm at run time. Each QLESCA agent evolves independently, and it has its own Q-table. The Q-table contains nine different states computed based on population density and distance from the micro population leader. As such, nine different actions are generated by Q-table to control QLESCA parameters, namely r(1) and r(3). These QLESCA parameters are responsible for adaptive switching from exploration/exploitation and vice versa. For each Q-table action, a reward value is given to a well-performing agent and a penalty to a non-performing agent. To verify the proposed algorithm's performance, QLESCA was evaluated with 23 continuous benchmarks, 20 large scale benchmark optimization functions, and three engineering design problems. In addition, the conducted analysis was compared with various SCA variant algorithms and other state-of-the-art swarm-based optimization methods. The numerical results demonstrate that the QLESCA was superior in terms of achieved fitness value. Statistical results confirm that QLESCA significantly outperforms other optimization algorithms. Additionally, the convergence curve outcomes show that the proposed QLESCA optimization obtains fast convergence against other conducted algorithms.
Methods for measuring convexity defects of compacts in Rn abound. However, none of the those measures seems to take into account continuity. Continuity in convexity measure is essential for optimization, stability ana...
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Parameter identification is one of most interesting fields for researchers in control engineering field. Different methods have been investigated in recent years. Methods can be divided into two main categories: mathe...
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ISBN:
(纸本)9780791883754
Parameter identification is one of most interesting fields for researchers in control engineering field. Different methods have been investigated in recent years. Methods can be divided into two main categories: mathematical based methods and artificial intelligence approaches. Mathematical approaches also known as traditional method which uses different formula to have an estimation of some missed parameters in practical systems, and on the other hand artificial intelligence methods uses different approaches for this purpose. This paper presents a new parameter identification method. A new modified evolutionary algorithm, which can be sorted as a approach of artificial intelligence, is presented and applied to a servo-hydraulic system as a parameter identification method. Coyote optimization algorithm is chosen for this purpose. The presented modified algorithm is changed in a way that in each iteration, uses details from previous steps and have a better performance in comparison with the basic algorithm. The proposed algorithm tries to update each candidate based on its previous condition which has been missed in basic algorithm. The proposed intelligent method is used as parameter identification method and applied to servo-hydraulic system. The results for simulation are given. Results show the efficiency of the presented method. Based on results, it can be drawn that the proposed method can be supposed as reliable method for nonlinear systems.
This paper presents a trajectory optimization algorithm for legged robotics that uses a novel cost function incorporating point cloud data to simultaneously optimize forfootstep locations and center of mass trajectori...
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ISBN:
(纸本)9780791859230
This paper presents a trajectory optimization algorithm for legged robotics that uses a novel cost function incorporating point cloud data to simultaneously optimize forfootstep locations and center of mass trajectories. This novel formulation transforms the inherently discrete problem of selecting footstep locations into a continuous cost. The algorithm seamlessly balances the desire to choose footstep locations that enhance the dynamic performance of the robot while still choosing locations that are viable and safe. We demonstrate the success of this algorithm by navigating the ALPHRED V2 robotic system over unknown terrain in a simulation environment.
This study attempts to establish a novel statistical and ANN-based decision-making mechanism for appraising hybrid system optimization strategies. An operational strategy and optimization problem for a hybrid, off-gri...
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This study attempts to establish a novel statistical and ANN-based decision-making mechanism for appraising hybrid system optimization strategies. An operational strategy and optimization problem for a hybrid, off-grid PV-wind system based on Nickel Iron battery storage is established in this paper, and an objective function for the system under consideration is presented. Particle swarm optimization, hybrid firefly and harmony search algorithm (HFAHS), Cultural Algorithm, harmony search, and Simulated Annealing are all used to address the optimization issue. A new procedure for selecting the optimal efficient optimization algorithm based on the OneWay ANOVA, the Tukey test, and ANN has been suggested, which allows for effective comparative analysis of algorithms. In the proposed approach, there were just a few steps to determine which algorithm would be the most successful in addressing the specific problem. A comparison of meta-heuristic optimization techniques based on ANN for the optimality of the presented system has been done. Comparing statistical parameters and ANN (i.e., MAE, MAPE, RMSE, and R-squared) to other models verifies the proposed model's efficiency. PSO's Rsquared was 99.7%, indicating more accurate predictions. Although the ANN is superior, the PSO algorithm surpasses all other statistically evaluated algorithms.
An algorithm for unconstrained non-convex optimization is described, which does not evaluate the objective function and in which minimization is carried out, at each iteration, within a randomly selected subspace. It ...
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The rate of convergence of the classical Thresholding Greedy Algorithm with respect to bases is studied in this paper. We bound the error of approximation by the product of both norms - the norm of f and the A1-norm o...
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