Search Results (3)

Aiming at the cooperative path-planning problem of multiple autonomous underwater vehicles in underwater three-dimensional terrain and dynamic ocean current environments, a hybrid algorithm based on the Improved Multi-Objective Particle Swarm Optimization (IMOPSO) and Dynamic Window (DWA) is proposed. The traditional particle swarm optimization algorithm is prone to falling into local optimization in high-dimensional and complex marine environments. It is difficult to meet multiple constraint conditions, the particle distribution is uneven, and the adaptability to dynamic environments is poor. In response to these problems, a hybrid initialization method based on Chebyshev chaotic mapping, pre-iterative elimination, and boundary particle injection (CPB) is proposed, and the particle swarm optimization algorithm is improved by combining dynamic parameter adjustment and a hybrid perturbation mechanism. On this basis, the Dynamic Window Method (DWA) is introduced as the local path optimization module to achieve real-time avoidance of dynamic obstacles and rolling path correction, thereby constructing a globally and locally coupled hybrid path-planning framework. Finally, cubic spline interpolation is used to smooth the planned path. Considering factors such as path length, smoothness, deflection Angle, and ocean current kinetic energy loss, the dynamic penalty function is adopted to optimize the multi-AUV cooperative collision avoidance and terrain constraints. The simulation results show that the proposed algorithm can effectively plan the dynamic safe path planning of multiple AUVs. By comparing it with other algorithms, the efficiency and security of the proposed algorithm are verified, meeting the navigation requirements in the current environment. Experiments show that the IMOPSO–DWA hybrid algorithm reduces the path length by 15.5%, the threat penalty by 8.3%, and the total fitness by 3.2% compared with the traditional PSO algorithm. Full article

Forest canopy image is a necessary technical means to obtain canopy parameters, whereas image segmentation is an essential factor that affects the accurate extraction of canopy parameters. To address the limitations of forest canopy image mis-segmentation due to its complex structure, this study proposes a forest canopy image segmentation method based on the parameter evolutionary barnacle optimization algorithm (PEBMO). The PEBMO algorithm utilizes an extensive range of nonlinear incremental penis coefficients better to balance the exploration and exploitation process of the algorithm, dynamically decreasing reproduction coefficients instead of the Hardy-Weinberg law coefficients to improve the exploitation ability; the parent generation of barnacle particles ($pl$ = 0.5) is subjected to the Chebyshev chaotic perturbation to avoid the algorithm from falling into premature maturity. Four types of canopy images were used as segmentation objects. Kapur entropy is the fitness function, and the PEBMO algorithm selects the optimal value threshold. The segmentation performance of each algorithm is comprehensively evaluated by the fitness value, standard deviation, structural similarity index value, peak signal-to-noise ratio value, and feature similarity index value. The PEBMO algorithm outperforms the comparison algorithm by $91.67\%,55.56\%,62.5\%,69.44\%$, and $63.89\%$ for each evaluation metric, respectively. The experimental results show that the PEBMO algorithm can effectively improve the segmentation accuracy and quality of forest canopy images. Full article

The Quantum Double Delta Swarm (QDDS) Algorithm is a networked, fully-connected novel metaheuristic optimization algorithm inspired by the convergence mechanism to the center of potential generated within a single well of a spatially colocated double–delta well setup. It mimics the wave nature of candidate positions in solution spaces and draws upon quantum mechanical interpretations much like other quantum-inspired computational intelligence paradigms. In this work, we introduce a Chebyshev map driven chaotic perturbation in the optimization phase of the algorithm to diversify weights placed on contemporary and historical, socially-optimal agents’ solutions. We follow this up with a characterization of solution quality on a suite of 23 single–objective functions and carry out a comparative analysis with eight other related nature–inspired approaches. By comparing solution quality and successful runs over dynamic solution ranges, insights about the nature of convergence are obtained. A two-tailed t-test establishes the statistical significance of the solution data whereas Cohen’s d and Hedge’s g values provide a measure of effect sizes. We trace the trajectory of the fittest pseudo-agent over all iterations to comment on the dynamics of the system and prove that the proposed algorithm is theoretically globally convergent under the assumptions adopted for proofs of other closely-related random search algorithms. Full article