Computational Intelligence
Z. K. Pourtaheri
Abstract
Background and Objectives: According to this fact that a typical autonomous underwater vehicle consumes energy for rotating, smoothing the path in the process of path planning will be especially important. Moreover, given the inherent randomness of heuristic algorithms, stability analysis of heuristic ...
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Background and Objectives: According to this fact that a typical autonomous underwater vehicle consumes energy for rotating, smoothing the path in the process of path planning will be especially important. Moreover, given the inherent randomness of heuristic algorithms, stability analysis of heuristic path planners assumes paramount importance.Methods: The novelty of this paper is to provide an optimal and smooth path for autonomous underwater vehicles in two steps by using two heuristic optimization algorithms called Inclined Planes system Optimization algorithm and genetic algorithm; after finding the optimal path by Inclined Planes system Optimization algorithm in the first step, the genetic algorithm is employed to smooth the path in the second step. Another novelty of this paper is the stability analysis of the proposed heuristic path planner according to the stochastic nature of these algorithms. In this way, a two-level factorial design is employed to attain the stability goals of this research.Results: Utilizing a Genetic algorithm in the second step of path planning offers two advantages; it smooths the initially discovered path, which not only reduces the energy consumption of the autonomous underwater vehicle but also shortens the path length compared to the one obtained by the Inclined Planes system optimization algorithm. Moreover, stability analysis helps identify important factors and their interactions within the defined objective function.Conclusion: This proposed hybrid method has implemented for three different maps; 36.77%, 48.77%, and 50.17% improvements in the length of the path are observed in the three supposed maps while smoothing the path helps robots to save energy. These results confirm the advantage of the proposed process for finding optimal and smooth paths for autonomous underwater vehicles. Due to the stability results, one can discover the magnitude and direction of important factors and the regression model.
Computational Intelligence
A. Rouhi; E. Pira
Abstract
Background and Objectives: This paper explores the realm of optimization by synergistically integrating two unique metaheuristic algorithms: the Wild Horse Optimizer (WHO) and the Fireworks Algorithm (FWA). WHO, inspired by the behaviors of wild horses, demonstrates proficiency in global exploration, ...
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Background and Objectives: This paper explores the realm of optimization by synergistically integrating two unique metaheuristic algorithms: the Wild Horse Optimizer (WHO) and the Fireworks Algorithm (FWA). WHO, inspired by the behaviors of wild horses, demonstrates proficiency in global exploration, while FWA emulates the dynamic behavior of fireworks, thereby enhancing local exploitation. The goal is to harness the complementary strengths of these algorithms, achieving a harmonious balance between exploration and exploitation to enhance overall optimization performance.Methods: The study introduces a novel hybrid metaheuristic algorithm, WHOFWA, detailing its design and implementation. Emphasis is placed on the algorithm's ability to balance exploration and exploitation. Extensive experiments, featuring a diverse set of benchmark optimization problems, including general test functions and those from CEC 2005, CEC 2019, and 2022, assess WHOFWA's effectiveness. Comparative analyses involve WHO, FWA, and other metaheuristic algorithms such as Reptile Search Algorithm (RSA), Prairie Dog Optimization (PDO), Fick’s Law Optimization (FLA), and Ladybug Beetle Optimization (LBO).Results: According to the Friedman and Wilcoxon signed-rank tests, for all selected test functions, WHOFWA outperforms WHO, FWA, RSA, PDO, FLA, and LBO by 42%, 55%, 74%, 71%, 48%, and 52%, respectively. Finally, the results derived from addressing real-world constrained optimization problems using the proposed algorithm demonstrate its superior performance when compared to several well-regarded algorithms documented in the literature.Conclusion: In conclusion, WHOFWA, the hybrid metaheuristic algorithm uniting WHO and FWA, emerges as a powerful optimization tool. Its unique ability to balance exploration and exploitation yields superior performance compared to WHO, FWA, and benchmark algorithms. The study underscores WHOFWA's potential in tackling complex optimization problems, making a valuable contribution to the realm of metaheuristic algorithms.
Computational Intelligence
M.R. Esmaeili; S.H. Zahiri; S.M. Razavi
Abstract
Background and Objectives: High-level synthesis (HLS) is one of the substantial steps in designing VLSI digital circuits. The primary purpose of HLS is to minimize the digital units used in the system to improve their power, delay, and area.Methods: In the modified MFO algorithm presented in this paper, ...
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Background and Objectives: High-level synthesis (HLS) is one of the substantial steps in designing VLSI digital circuits. The primary purpose of HLS is to minimize the digital units used in the system to improve their power, delay, and area.Methods: In the modified MFO algorithm presented in this paper, a hyperbolic spiral is chosen as the update mechanism of moths. Also, by presenting a new approach, a paramount issue involved in applying meta-heuristic methods for solving HLS problems of VLSI circuits has been disentangled.Results: By comparing the performance of the proposed method with Genetic algorithm (GA)-based method and particle swarm optimization (PSO)-based method for the synthesis of the digital filters, it is concluded that the proposed method has the higher ability in the HLS of data path in digital filters. The best improvement is 2.78% for the delay (latency), 6.51% for the occupied area of the chip and 6.93% in power consumption. Another feature of the proposed method is its high-speed in finding optimal solutions, in a manner which, more than 21.6% and 12.9% faster than the GA-based and PSO-based methods, respectively on average.Conclusion: The most important very large scale integration (VLSI) circuits are digital filters and transformers, which are widely used in audio and video processing, medical signal processing, and telecommunication systems. The complex, expansive, and discrete nature of design space in high-level synthesis problems has made them one of the most difficult problems in VLSI circuit design.
