Balancing and sequencing problem of mixed-model U-shaped robotic assembly line: Mathematical model and dragonfly algorithm based approach

A new strategy is proposed to find optimal fibers orientation of stiffened laminated composite panels to reach their maximum buckling load. To do so, buckling analysis of laminated stiffened composite panel is performed by using the ABAQUS software. A new hybrid algorithm based on the particle swarm optimization (PSO) and genetic algorithm (GA) is introduced for the optimization purpose. The conventional PSO method is improved by introducing a new inertia weight of velocity in the formulation. The validity of the proposed modeling technique is controlled by comparing the modeling results with that of experiment, and other previously published models and a satisfactory result has been achieved. Also, it has been found that the proposed optimization method has better performance than the existing methods, in terms of convergence speed and the accuracy of the algorithm. Moreover, the effects of shape of stiffeners, number of panel layers, curvature of panel, and boundary conditions on the optimal design are investigated.

Maximum buckling load of stiffened laminated composite panel by an improved hybrid PSO-GA optimization technique

Traveling salesman problem (TSP) has been widely applied to various fields of production and life. In order to solve the TSP, this work optimizes the architecture of the particle swarm optimization (PSO) by introducing a preprocessing multi-subdomain grouping approach to divide the entire search space into several sub-regions, based on the geographical center of planned cities. To solve subdomain problems, the global search ability of PSO is further improved using genetic mutation and particle competition mechanism to maintain the population diversity during late iterations. An effective simplified 4-opt is finally employed to eliminate path-crossing after connecting all subdomain paths in a time saving way. Comparison with three of our previous algorithms and seven algorithms from other reports reveals that the proposed algorithm shows good performance in both the computing efficiency and route quality. Particularly, it is suitable for small (or medium)-sized TSP problems with relatively percentage error values below 4.8%.

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Multi-Subdomain Grouping-Based Particle Swarm Optimization for the Traveling Salesman Problem

A multi-objective bat algorithm with a novel competitive mechanism and its application in controller tuning

Axial mechanical properties and robust optimization of foam-filled hierarchical structures

Design optimization of moderately thick hexagonal honeycomb sandwich plate with modified multi-objective particle swarm optimization by genetic algorithm (MOPSOGA)

A.R. Namvar

Papers

Design optimization of moderately thick hexagonal honeycomb sandwich plate with modified multi-objective particle swarm optimization by genetic algorithm (MOPSOGA)