Swarming genetic algorithm: A nested fully coupled hybrid of genetic algorithm and particle swarm optimization

Abstract

Particle swarm optimization and genetic algorithms are two classes of popular heuristic algorithms that are frequently used for solving complex multi-dimensional mathematical optimization problems, each one with its one advantages and shortcomings. Particle swarm optimization is known to favor exploitation over exploration, and as a result it often converges rapidly to local optima other than the global optimum. The genetic algorithm has the ability to overcome local extrema throughout the optimization process, but it often suffers from slow convergence rates. This paper proposes a new hybrid algorithm that nests particle swarm optimization operations in the genetic algorithm, providing the general population with the exploitation prowess of the genetic algorithm and a sub-population with the high exploitation capabilities of particle swarm optimization. The effectiveness of the proposed algorithm is demonstrated through solutions of several continuous optimization problems, as well as discrete (traveling salesman) problems. It is found that the new hybrid algorithm provides a better balance between exploration and exploitation compared to both parent algorithms, as well as existing hybrid algorithms, achieving consistently accurate results with relatively small computational cost.

Joint Task Offloading and Resource Allocation in Vehicular Edge Computing Networks for Emergency Logistics

“…Aivaliotis-Apostolopoulos and Loukidis proposed a hybrid algorithm that nests particle swarm optimization operations in the genetic algorithm, which is named the swarming genetic algorithm (SGA) [28]. Examined by continuous optimization problems and discrete (traveling salesman) problems, the SGA has overall signifcantly better performance than PSO, GA, and two existing hybrid algorithms in terms of accuracy.…”

Pharmaceutical distribution path optimization based on K-nearest neighbor algorithm

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“…The basic idea of the algorithm [6][7][8] is to start from the starting point and select the closest and unvisited point to the current point as the next visited point each time until all points have been visited and then return to the starting point. The main advantages of this algorithm [9] are that it is simple to understand, easy to implement, and can find acceptable solutions for small and medium scale problems.…”

Design of a Multi-Mode Mechanical Finger Based on Linkage and Tendon Fusion Transmission

“…The grasping force is required to be higher during operation; the proper grasping force is the key to ensuring stability and reliability when grasping objects [ 21 , 22 ]. Therefore, based on the kinematics and statics analysis, we take the grasping force as the optimization target and use the genetic algorithm to optimize the finger mechanism parameters [ 23 , 24 , 25 , 26 ]. Firstly, according to the human phalanx length and thickness, the values of , , and are set as 42 mm, 50 mm, and 8 mm, respectively.…”