Solving Competitive Traveling Salesman Problem Using Gray Wolf Optimization Algorithm

In this paper a Gray Wolf Optimization (GWO) algorithm is presented to solve the Competitive Traveling Salesman Problem (CTSP). In CTSP, there are numbers of non-cooperative salesmen their goal is visiting a larger possible number of cities with lowest cost and most gained benefit. Each salesman will get a benefit when he visits unvisited city before all other salesmen. Two approaches have been used in this paper, the first one called static approach, it is mean evenly divides the cities among salesmen. The second approach is called parallel at which all cities are available to all salesmen and each salesman tries to visit as much as possible of the unvisited cities. The algorithms are executed for 1000 times and the results prove that the GWO is very efficient giving an indication of the superiority of GWO in solving CTSP

Evolutionary Algorithms, Markov Decision Processes, Adaptive Critic Designs, and Clustering: Commonalities, Hybridization and Performance

We briefly review and compare the mathematical formulation of Markov decision processes (MDP) and evolutionary algorithms (EA). In so doing, we observe that the adaptive critic design (ACD) approach to MDP can be viewed as a special form of EA. This leads us to pose pertinent questions about possible expansions of the methodology of ACD. This expansive view of EA is not limited to ACD. We discuss how it is possible to consider the powerful chained Lin Kernighan (chained LK) algorithm for the traveling salesman problem (TSP) as a degenerate case of EA. Finally, we review some recent TSP results, using clustering to divide-and-conquer, that provide superior speed and scalability

MO-MFCGA: Multiobjective multifactorial cellular genetic algorithm for evolutionary multitasking

Multiobjetive optimization has gained a considerable momentum in the evolutionary computation scientific community. Methods coming from evolutionary computation have shown a remarkable performance for solving this kind of optimization problems thanks to their implicit parallelism and the simultaneous convergence towards the Pareto front. In any case, the resolution of multiobjective optimization problems (MOPs) from the perspective of multitasking optimization remains almost unexplored. Multitasking is an incipient research stream which explores how multiple optimization problems can be simultaneously addressed by performing a single search process. The main motivation behind this solving paradigm is to exploit the synergies between the different problems (or tasks) being optimized. Going deeper, we resort in this paper to the also recent paradigm Evolutionary Multitasking (EM). We introduce the adaptation of the recently proposed Multifactorial Cellular Genetic Algorithm (MFCGA) for solving MOPs, giving rise to the Multiobjective MFCGA (MO-MFCGA). An extensive performance analysis is conducted using the Multiobjective Multifactorial Evolutionary Algorithm as comparison baseline. The experimentation is conducted over 10 multitasking setups, using the Multiobjective Euclidean Traveling Salesman Problem as benchmarking problem. We also perform a deep analysis on the genetic transferability among the problem instances employed, using the synergies among tasks aroused along the MO-MFCGA search procedure

Towards the Design of Heuristics by Means of Self-Assembly

The current investigations on hyper-heuristics design have sprung up in two different flavours: heuristics that choose heuristics and heuristics that generate heuristics. In the latter, the goal is to develop a problem-domain independent strategy to automatically generate a good performing heuristic for the problem at hand. This can be done, for example, by automatically selecting and combining different low-level heuristics into a problem specific and effective strategy. Hyper-heuristics raise the level of generality on automated problem solving by attempting to select and/or generate tailored heuristics for the problem at hand. Some approaches like genetic programming have been proposed for this. In this paper, we explore an elegant nature-inspired alternative based on self-assembly construction processes, in which structures emerge out of local interactions between autonomous components. This idea arises from previous works in which computational models of self-assembly were subject to evolutionary design in order to perform the automatic construction of user-defined structures. Then, the aim of this paper is to present a novel methodology for the automated design of heuristics by means of self-assembly

ON-LINE NETWORK SCHEDULING IN EMERGENCY OPERATION FOR MEDICAL RESOURCES WITH SINGLE-PROCESSOR SINGLE-DESTINATION

Emergency Management has received more and more attention in the recent years. Most research in this eld focused on evacuation of victims from dangerous places to safe places, but little on allocation of medical resources to safe places and/or transportation tools to the dangerous places. This thesis studies the problem of delivering medical resources from medical centers to the temporary aid site in a disaster-a ected area to help the wounded victims. In particular, this thesis describes a new algorithm for solving this problem. As requirements of medical resources for a disaster a ected area are not known in advance, the problem is in the so-called on-line environment. The algorithm for such a problem is also called on-line algorithm. The evaluation criterion for such an on-line algorithm is the so-called competitive ratio. This thesis considers four cases of such a problem: (1) the capacity of vehicles for transporting medical resources and the number of vehicles are both in nite, (2) the capacity of vehicles is in nite but the number of vehicles is one, (3) the capacity of vehicles is nite and the number of vehicles is in nite, (4) the capacity of vehicles is nite and the number of vehicles is one. Algorithms for the four cases are called H1, H2, H3, and H4, ii respectively. For all these cases, this thesis presents properties, appropriate on-line algorithms and theoretical analysis of these algorithms. The result of the analysis shows that H1 and H3 are optimal based on the competitive ratio criterion while the other two have a very small gap in terms of the optimum criterion. The thesis also presents a case study for having a sense of the performance of H2 and demonstrating practicality of the developed algorithms. The result of this thesis has contributions to the eld of resource planning and scheduling and has application in not only emergency management but also supply chain management in manufacturing and construction

Determine The Optimal Sequence - dependent Setup Cost and / or Setup Time for Single Demand with Multiple Products Using Modified Assignment Method

Sequencing is the most impact factor on the total setup cost and / or time and the products sequences inside demands that consist from muti-products . It is very important in assembly line and batch production . The most important drawback of existing methods used to solve the sequencing problems is the sequence must has a few products and dependent setup cost or setup time . In this paper we modify the assignment method –based goal programming method to minimize the setup cost and / or setup time. The main advantage of this new method , it is not affected by the number of products in the sequence and can treatment the sequence problems with two or more objectives . Keywords: products sequences , setup cost , setup time , travel salesman problem (TSP ) , modified assignment method , goal programming