Improvements on the bees algorithm for continuous optimisation problems

This work focuses on the improvements of the Bees Algorithm in order to enhance the algorithm’s performance especially in terms of convergence rate. For the first enhancement, a pseudo-gradient Bees Algorithm (PG-BA) compares the fitness as well as the position of previous and current bees so that the best bees in each patch are appropriately guided towards a better search direction after each consecutive cycle. This method eliminates the need to differentiate the objective function which is unlike the typical gradient search method. The improved algorithm is subjected to several numerical benchmark test functions as well as the training of neural network. The results from the experiments are then compared to the standard variant of the Bees Algorithm and other swarm intelligence procedures. The data analysis generally confirmed that the PG-BA is effective at speeding up the convergence time to optimum. Next, an approach to avoid the formation of overlapping patches is proposed. The Patch Overlap Avoidance Bees Algorithm (POA-BA) is designed to avoid redundancy in search area especially if the site is deemed unprofitable. This method is quite similar to Tabu Search (TS) with the POA-BA forbids the exact exploitation of previously visited solutions along with their corresponding neighbourhood. Patches are not allowed to intersect not just in the next generation but also in the current cycle. This reduces the number of patches materialise in the same peak (maximisation) or valley (minimisation) which ensures a thorough search of the problem landscape as bees are distributed around the scaled down area. The same benchmark problems as PG-BA were applied against this modified strategy to a reasonable success. Finally, the Bees Algorithm is revised to have the capability of locating all of the global optimum as well as the substantial local peaks in a single run. These multi-solutions of comparable fitness offers some alternatives for the decision makers to choose from. The patches are formed only if the bees are the fittest from different peaks by using a hill-valley mechanism in this so called Extended Bees Algorithm (EBA). This permits the maintenance of diversified solutions throughout the search process in addition to minimising the chances of getting trap. This version is proven beneficial when tested with numerous multimodal optimisation problems

Simulation And Reproduction Of A Manipulator According To Classical Arm Representation And Trajectory Planning

The technical and vocational institutions are the key feeders for skilled human capital in the robotic revolution economy. It is essential to engage the students by creating new, affordable robotics at a fraction of the cost. This study presents the design and simulation of a six-axis robot manipulator specifically made for education and training. The robot was developed based on Chriss-Annin’s configuration. The robot arm was printed using Fused Deposition Modelling technique using the acrylonitrile butadiene styrene filament. Before it was constructed, the arm parameters were assessed using Scilab as the tool and the Ntraditional and fundamental methods: the Denevit-Hartenberg representation, the forward kinematics, the inverse kinematics, and the trajectory planning. The outcomes showed that the arm was working well on positioning and path planning. Therefore, the complete assembly of the robot should be able to assume a role in education and training. This work is an extension of the paper entitled “Lightweight Robot Manipulator for TVET Training using FDM Technique” published in 2018 Symposium on Electrical, Mechatronics and Applied Science 2018 (SEMA 2018)