An improved adaptive genetic algorithm for mobile robot path planning analogous to TSP with constraints on city priorities

The material transportation planning with a mobile robot can be regarded as the ordered clustered traveling salesman problem. To solve such problems with different priorities at stations, an improved adaptive genetic simulated annealing algorithm is proposed. Firstly, the priority matrix is defined according to station priorities. Based on standard genetic algorithm, the generating strategy of the initial population is improved to prevent the emergence of non-feasible solutions, and an improved adaptive operator is introduced to improve the population ability for escaping local optimal solutions and avoid premature phenomena. Moreover, to speed up the convergence of the proposed algorithm, the simulated annealing strategy is utilized in mutation operations. The experimental results indicate that the proposed algorithm has the characteristics of strong ability to avoid local optima and the faster convergence speed

Практичне використання методу генетичних алгоритмів для розв'язання задачі комівояжера в геоінформаційних системах

У статті проведено дослідження та адаптацію генетичного алгоритму як евристичного методу для розв’язання задачі комівояжера. Алгоритм застосовано у вигляді web-сервісу геоінформаційної системи з використанням картографічного інтерфейсу Яндекс.Карт.В статье проведено исследование и адаптацію генетического алгоритма как эврестического метода для решения задачи коммивояжера. Алгоритм применено в виде web-сервиса геоинформационной системы реализованного на базе интерфейса Яндекс.Карт.The paper investigated and improved the genetic algorithm like an heuristic method for solving the traveling salesman problem. Algorithm implemented as a web-service in framework of geographic information system based on Yandex.Maps interface

Multi-Stop Routing Optimization: A Genetic Algorithm Approach

In this research, we investigate and propose new operators to improve Genetic Algorithm’s performance to solve the multi-stop routing problem. In a multi-stop route, a user starts at point x, visits all destinations exactly once, and then return to the same starting point. In this thesis, we are interested in two types of this problem. The first type is when the distance among destinations is fixed. In this case, it is called static traveling salesman problem. The second type is when the cost among destinations is affected by traffic congestion. Thus, the time among destinations changes during the day. In this case, it is called time-dependent traveling salesman problem. This research proposes new improvements on genetic algorithm to solve each of these two optimization problems. First, the Travelling Salesman Problem (TSP) is one of the most important and attractive combinatorial optimization problems. There are many meta-heuristic algorithms that can solve this problem. In this paper, we use a Genetic Algorithm (GA) to solve it. GA uses different operators: selection, crossover, and mutation. Sequential Constructive Crossover (SCX) and Bidirectional Circular Constructive Crossover (BCSCX) are efficient to solve TSP. Here, we propose a modification to these crossovers. The experimental results show that our proposed adjustment is superior to SCX and BCSCX as well as to other conventional crossovers (e.g. Order Crossover (OX), Cycle Crossover (CX), and Partially Mapped Crossover (PMX)) in term of solution quality and convergence speed. Furthermore, the GA solver, that is improved by applying inexpensive local search operators, can produce solutions that have much better quality within reasonable computational time. Second, the Time-Dependent Traveling Salesman Problem (TDTSP) is an interesting problem and has an impact on real-life applications such as a delivery system. In this problem, time among destinations fluctuates during the day due to traffic, weather, accidents, or other events. Thus, it is important to recommend a tour that can save driver’s time and resources. In this research, we propose a Multi-Population Genetic Algorithm (MGA) where each population has different crossovers. We compare the proposed MG against Single-Population Genetic Algorithm (SGA) in terms of tour time solution quality. Our finding is that MGA outperforms SGA. Our method is tested against real-world traffic data [1] where there are 200 different instances with different numbers of destinations. For all tested instances, MGA is superior on average by at least 10% (for instances with size less than 50) and 20% (for instances of size 50) better tour time solution compared to SGA with OX and SGA with PMX operators, and at least 4% better tour time compared toga with SCX operator

Genetic Algorithm with Evolutionary Chain-Based Mutation and Its Applications

Mutation is one of the important operators in genetic algorithm. In traditional genetic algorithm, mutation is activated stochastically. In this way it is unknown and cannot be controlled for which individuals to be mutated. Therefore, it is unavoidable that some good individuals are destroyed by mutation and then the evolutionary efficiency of the genetic algorithm is dampened. Owing to this kind of destructivity of mutation, the operator of mutation has to be limited within a very small probability, and the potentiality of mutation is consequently limited. In this paper, we present an evolutionary chain-based mutation and a control strategy of reasonable competition, in which the heuristic information provided by the evaluation function is well utilized. This method avoids the blindness of stochastic mutation. The performance improved in this method is shown by two examples, a fuzzy modeling for the identification of a nonlinear function and a typical combinatorial optimization problem-the traveling salesman problem

Optimization of a Sequence for Multi-Cutting Tool Operations in CNC Machines

Optimizing the sequence of operations (SOS) is important factor in improving the productivity of machines and reducing the production cost. However, CNC programing packages are available and have been significantly improved program generation for CNC machines, but in most of these packages the SOS optimization is not considered. This paper, introduce an efficient solution to optimized SOS for multi-cutting tools operations located asymmetrically. A well-known genetic algorithm (GA) is utilized to solve the SOS problem. After optimizing SOS, its related CNC program will be generated. The operation locations are considered as cities of Traveling Salesman Problem (TSP), and the cutting tool is considered as traveling salesman, by this way, TSP methodology can be modified to formulate the SOS problem. GA and the modified TSP can be incorporated into CNC programming packages to optimize the SOS before the program generation