A new global best guided artificial bee colony algorithm with application in robot path planning

An Improved Beetle Swarm Optimization Algorithm for the Intelligent Navigation Control of Autonomous Sailing Robots

“…Genetic Algorithm [23] Probability-Based Incremental Learning [24] Evolution Strategies [25] Biogeography-Based Optimizer [26] Genetic Programming [27] Physics-based Algorithms Simulated Annealing [28] Gravitational Local Search [29] Curved Space Optimization [30] Charged System Search [31] Small-World Optimization Algorithm [32] Central Force Optimization [33] Artificial Chemical Reaction Optimization Algorithm [34] Big-Bang Big-Crunch Curved Space Optimization [35] Gravitational Search Algorithm [36] Black Hole Algorithm [37] Ray Optimization Algorithm [38] Swarm-based Algorithms Particle Swarm Optimization Algorithm [39] Ant Colony Optimization [40] Artificial Bee Colony [41] Fruit Fly Optimization Algorithm [42] Artificial Fish-Swarm Algorithm [43] Gray Wolf Optimization [44] Human-based Algorithms Seeker Optimization Algorithm [45] Group Search Optimizer [46] League Championship [47] Algorithm (LCA) Social-Based Algorithm [48] Mine Blast Algorithm [49] Harmony Search [50] called multirobot MOPSO. It considers shortness, safety, and smoothness.…”

“…Bees perform different activities according to their respective division of labor and share and communicate bee colony information to find the optimal solution to the problem. Xu [53] introduced the coevolution framework into ABC and designed a leading artificial bee colony algorithm, which has an improved strategy that can quicken its convergence and overcome size dependence. The GWO is a swarm intelligence optimization algorithm.…”

“…The third group of methods inspired by nature includes group techniques that mimic the social behavior of animal groups. The most popular algorithms are the particle swarm optimization algorithm (PSO) [39], ant colony optimization (ACO) [40], artificial bee colony (ABC) [41], the fruit fly optimization algorithm (FOA) [42], the artificial fish-swarm algorithm (AFSA) [43], and gray wolf optimization (GWO) [44].…”

One‐dimensional convolutional neural networks for high‐resolution range profile recognition via adaptively feature recalibrating and automatically channel pruning

“…3.3.3 | Onlooker bee phase (Line [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31] In this phase, we use the global best position of nectar source to act as a guide for the onlooker bees to update C { ′} j j n =1 , to replace the Equation 20, the search equation is updated as follows:…”

A Novel Path Planning Algorithm for Warehouse Robots Based on a Two-Dimensional Grid Model

“…To reduce calculation time, many heuristic methods represented by evolutionary computation have been proposed. Evolutionary computation is a kind of adaptive optimization probabilistic search algorithm, mainly including GA [6]- [8], ACO [9]- [12], improved ACO [12]- [15], particle swarm optimization [16], [17], improved particle swarm optimization [18]- [20], and artificial bee colony [21], [22] etc.…”