Effectiveness of Selection Mechanisms on the efficiency of Multi Parent Crossover Operator

Multi-parent crossover has been proven its ability to address many of combinatorial optimization problems such as the traveling salesman problem and the vehicle routing problem with time windows. The successful use of multi-parent crossover arises from its abilities to enhance the search performance via utilizing information exchanged by more than two parents and inheriting by offspring. These parents are selected according to one of the selection mechanisms. Selecting the most appropriate parents for a crossover process might leads to improving the effectiveness of genetic algorithm. Therefore, this work investigates the effect of selection mechanism on the efficiency of multi-parent crossover. To test this, seven selection mechanisms have been used; random selection mechanism, roulette wheel mechanism, stochastic universal sampling mechanism, tournament selection mechanism, best selection mechanism, single best-couple random selection mechanism and couple best- single random selection mechanism. The performance of the proposed algorithm is tested using Solomon VRPTW benchmark. The experimental results show the superiority of multi-parent crossover that employs the selection mechanism which selects the outstanding individuals to form most of parents over multi-parent crossover that employ other selection mechanisms. This demonstrates the efficiency of employing best parents in a crossover process that can assist the search process to attain a better solution

Dual Performance Optimization of 6-DOF Robotic Arm Trajectories in Biomedical Applications

For the first time, dual-performance perfection technologies were used to kinematically operate sophisticated robots. In this study, the trajectory development of a robot arm is optimized using a dual-performance perfection technique. The proposed approach alters the robot arm's Kinematics by creating virtual points even if the robotic system is not redundant to make it kinematically suitable for biomedical applications. In the suggested method, an appropriate objective function is chosen to raise one or maybe more performance measures while lowering one or more kinematic characteristics of a robot arm. The robot arm's end effector is set in place at the crucial locations, and the dual performance precision algorithm changes the joints and virtual points due to the robot arm's self-motion. As a result, the ideal values for the virtual points are established, and the robot arm's design is changed. Accordingly, this method's ability to visualize modifications made to the processor's design during the optimization problem is one of its benefits. The active robotic arm is used as a case study in this article. The task is defined as choosing the best path based on the input target's position and direction and is used in X-ray robot systems. The outcomes demonstrate the viability of the suggested approach and can serve as a useful prototype for an intelligent X-ray robot

A hybrid meta-heuristic algorithm for vehicle routing problem with time windows

Harmony search algorithm, which simulates the musical improvisation process in seeking agreeable harmony, is a population based meta-heuristics algorithm for solving optimization problems. Although it has been successfully applied on various optimization problems; it suffers the slow convergence problem, which greatly hinders its applicability for getting good quality solution. Therefore, in this work, we propose a hybrid meta-heuristic algorithm that hybridizes a harmony search with simulated annealing for the purpose of improving the performance of harmony search algorithm. Harmony search algorithm is used to explore the search spaces. Whilst, simulated annealing algorithm is used inside the harmony search algorithm to exploit the search space and further improve the solutions that are generated by harmony search algorithm. The performance of the proposed algorithm is tested using the Solomon's Vehicle Routing Problem with Time Windows (VRPTW) benchmark. Numerical results demonstrate that the hybrid approach is better than the harmony search without simulated annealing and the hybrid also proves itself to be more competent (if not better on some instances) when compared to other approaches in the literature. </jats:p