Using genetic algorithms for the single allocation hub location problem

Hub location problem is an NP-hard problem that frequently arises in the design of transportation and distribution systems, postal delivery networks, and airline passenger flow. This work focuses on the Single Allocation Hub Location Problem (SAHLP). Genetic Algorithms (GAs) for the capacitated and uncapacitated variants of the SAHLP based on new chromosome representations and crossover operators are explored. The GAs is tested on two well-known sets of real-world problems with up to 200 nodes. The obtained results are very promising. For most of the test problems the GA obtains improved or best-known solutions and the computational time remains low. The proposed GAs can easily be extended to other variants of location problems arising in network design planning in transportation systems

Navigational control of multiple mobile robots in various environments

The thesis addresses the problem of mobile robots navigation in various cluttered environments and proposes methodologies based on a soft computing approach, concerning to three main techniques: Potential Field technique, Genetic Algorithm technique and Fuzzy Logic technique. The selected techniques along with their hybrid models, based on a mathematical support, solve the three main issues of path planning of robots such as environment representation, localization and navigation. The motivation of the thesis is based on some cutting edge issues for path planning and navigation capabilities, that retrieve the essential for various situations found in day-to-day life. For this purpose, complete algorithms are developed and analysed for standalone techniques and their hybrid models. In the potential field technique the local minima due to existence of dead cycle problem has been addressed and the possible solution for different situations has been carried out. In fuzzy logic technique the different controllers have been designed and their performance analysis has been done during their navigational control in various environments. Firstly, the fuzzy controller having all triangular members with five membership functions have been considered. Subsequently the membership functions are changed from Triangular to other functions, e.g. Trapezoidal, Gaussian functions and combinational form to have a more smooth and optimised control response. It has been found that the fuzzy controller with all Gaussian membership function works better compared to other chosen membership functions. Similarly the proposed Genetic algorithm is based on the suitable population size and fitness functions for finding out the robot steering angle in various cluttered field. At the end hybrid approaches e.g. Potential-Fuzzy, otential-Genetic, Fuzzy-Genetic and Potential-Fuzzy-Genetic are considered for navigation of multiple mobile robots. Initially the combination of two techniques has been selected in order to model the controllers and then all the techniques have been hybridized to get a better controller. These hybrid controllers are first designed and analysed for possible solutions for various situations provided by human intelligence. Then computer simulations have been executed extensively for various known and unknown environments. The proposed hybrid algorithms are embedded in the controllers of the real robots and tested in realistic scenarios to demonstrate the effectiveness of the developed controllers. Finally, the thesis concludes in a chapter describing the comparison of results acquired from various environments, showing that the developed algorithms achieve the main goals proposed by different approaches with a high level of simulations. The main contribution provided in the thesis is the definition and demonstration of the applicability of multiple mobile robots navigations with multiple targets in various environments based on the strategy of path optimisation