Survey of Metaheuristic Algorithms for Combinatorial Optimization <s> Ant Colony Optimization <s> The study of natural processes has inspired several heuristic optimization algorithms which have proved to be very effective in combinatorial optimization. In this paper we show how a new heuristic called ant system, in which the search task is distributed over many simple, loosely interacting agents, can be successfully applied to find good solutions of job-shop scheduling problems. <s> BIB001 </s> Survey of Metaheuristic Algorithms for Combinatorial Optimization <s> Ant Colony Optimization <s> In this article we present an Ant Colony Optimization approach to the Flow Shop Problem. ACO is a new algorithmic approach, inspired by the foraging behavior of real ants, that can be applied to the solution of combinatorial optimization problems. Artificial ants are used to construct solutions for Flow Shop Problems that subsequently are improved by a local search procedure. Comparisons with other heuristics for the Flow Shop Scheduling problem show that with our approach very promising results are obtained. <s> BIB002 </s> Survey of Metaheuristic Algorithms for Combinatorial Optimization <s> Ant Colony Optimization <s> The Ant System is a distributed metaheuristic that combines an adaptive memory with alocal heuristic function to repeatedly construct solutions of hard combinatorial optimizationproblems. In this paper, we present an improved ant system algorithm for the Vehicle RoutingProblem with one central depot and identical vehicles. Computational results on fourteenbenchmark problems from the literature are reported and a comparison with five othermetaheuristic approaches for solving Vehicle Routing Problems is given. Copyright Kluwer Academic Publishers 1999 <s> BIB003 </s> Survey of Metaheuristic Algorithms for Combinatorial Optimization <s> Ant Colony Optimization <s> Abstract This research applies the meta-heuristic method of ant colony optimization (ACO) to an established set of vehicle routing problems (VRP). The procedure simulates the decision-making processes of ant colonies as they forage for food and is similar to other adaptive learning and artificial intelligence techniques such as Tabu Search, Simulated Annealing and Genetic Algorithms. Modifications are made to the ACO algorithm used to solve the traditional traveling salesman problem in order to allow the search of the multiple routes of the VRP. Experimentation shows that the algorithm is successful in finding solutions within 1% of known optimal solutions and the use of multiple ant colonies is found to provide a comparatively competitive solution technique especially for larger problems. Additionally, the size of the candidate lists used within the algorithm is a significant factor in finding improved solutions, and the computational times for the algorithm compare favorably with other solution methods. <s> BIB004 </s> Survey of Metaheuristic Algorithms for Combinatorial Optimization <s> Ant Colony Optimization <s> The vehicle routing problem (VRP), a well-known combinatorial optimization problem, holds a central place in logistics management. This paper proposes an improved ant colony optimization (IACO), which possesses a new strategy to update the increased pheromone, called ant-weight strategy, and a mutation operation, to solve VRP. The computational results for fourteen benchmark problems are reported and compared to those of other metaheuristic approaches. <s> BIB005 </s> Survey of Metaheuristic Algorithms for Combinatorial Optimization <s> Ant Colony Optimization <s> Ant Colony Optimization (ACO) is a kind of metaheuristic that has become very popular for solving problems from combinatorial optimization. Solutions for a given problem are constructed by a random walk on a so-called construction graph. This random walk can be influenced by heuristic information about the problem. In contrast to many successful applications, the theoretical foundation of this kind of metaheuristic is rather weak. Theoretical investigations with respect to the runtime behavior of ACO algorithms have been started only recently for the optimization of pseudo-Boolean functions. We present the first comprehensive rigorous analysis of a simple ACO algorithm for a combinatorial optimization problem. In our investigations, we consider the minimum spanning tree (MST) problem and examine the effect of two construction graphs with respect to the runtime behavior. The choice of the construction graph in an ACO algorithm seems to be crucial for the success of such an algorithm. First, we take the input graph itself as the construction graph and analyze the use of a construction procedure that is similar to Broder's algorithm for choosing a spanning tree uniformly at random. After that, a more incremental construction procedure is analyzed. It turns out that this procedure is superior to the Broder-based algorithm and produces additionally in a constant number of iterations an MST, if the influence of the heuristic information is large enough. <s> BIB006 </s> Survey of Metaheuristic Algorithms for Combinatorial Optimization <s> Ant Colony Optimization <s> In this paper, we present a genetic algorithm and ant colony optimization algorithm for solving the Job-shop Scheduling Problem (JSSP). The genetic algorithm comprises of different stages like generating the initial population, selecting the individuals for reproduction and reproducing new individuals. Ant Colony Optimization (ACO) is a metaheuristic inspired by the foraging behavior of ants, which is also used to solve this combinatorial optimization problem. In JSSP ants move from one machine (nest) to another machine (food source) depending upon the job flow, thereby optimizing the sequence of jobs. The sequence of jobs is scheduled using Fuzzy logic and optimizes using GA and ACO. The makespan, completion time, makespan efficiency, algorithmic efficiency and the elapsed time for the genetic algorithm and the ant colony algorithm are evaluated and compared. The improvement in the performance of the algorithms based on the computed parameters is also discussed in this paper. Computational results of these optimization algorithms are compared by analyzing the JSSP benchmark instances, FT10 and the ABZ10 problems. <s> BIB007
Ant colony optimization (ACO) was proposed by Dorigo in1991, inspired by the foraging behavior of real ants  . Ants can find the shortest path from the nest to food source through a chemical substance called pheromone. As ants move, a certain amount of pheromone is layered on the ground making a pheromone trail. As the ants follow the trail, they will be influenced by the trail even more. This process involves a positive reinforcement loop in which the probability that ants will visit the path in the future is directly proportional to the number of ants that previously visited it. It has been proved experimentally that this procedure can give rise to growth of shortest paths. Here, at each cycle, solution is created in a randomized and greedy way by a number of artificial ants. However, Heuristic evaluation and the amount of pheromone are the one by which an individual ant chooses the next element to be included into current partial solution. When a complete solution is built, the procedure is restarted with the updated pheromone levels. This is repeated for a fixed amount of cycles or until search stagnation occurs. M. BIB001 proposed a new heuristic called ant system for solving job shop scheduling problem. In this, search task is spread over many simple, loosely interacting agents which can be used to find good solutions BIB001 . Dorigo and Gambardella (1997) proposed a distributed algorithm ant colony system for solving traveling salesman problem. In this, a group of agents cooperates using an indirect form of communication to find the best solution to the traveling salesman problem  . Thomas (1998) proposed ACO based on Max-Min ant system to solve flow shop problem. MaxMin ant system is enhanced by a fast local search procedure to produce high quality solutions to the problem BIB002 . BIB003 proposed an ant system for solving vehicle routing problem. An ant system is a distributed metaheuristic that merges an adaptive memory with a local heuristic function to frequently construct solutions of the problems BIB003 .  proposed a parallel model of ACO to solve the quadratic assignment problem. The parallel ant colony optimization algorithm has been merged with a local search method that depends on a tabu search  . BIB004 proposed an ant colony optimization technique for the vehicle routing problem. It has been proved experimentally that the algorithm is successful in finding a competitive solution technique especially for larger problems BIB004 . BIB006 proposed an ant colony optimization for minimum spanning tree problem. Solution to the problem is built by a random walk on a so called construction graph. This random walk can be determined by the heuristic information BIB006 . Yu bin et al. proposes an ant colony optimization for solving the vehicle routing problem. The proposed algorithm owns a new scheme by modifying the increased pheromone called ant weight strategy and using a mutation operator to solve the vehicle routing problem BIB005 . Sureka et al. (2010) proposed an ant colony optimization for solving job shop scheduling problem. In this, fuzzy logic is used for scheduling the sequence of job and it can be optimized using ACO BIB007 . Zar chi Su Su Hlaing and May Aye Khine (2011) proposed an ACO for solving travelling salesman problem in which, good distribution strategy and information entropy is conducted. Also, it uses local optimization heuristic for solving traveling salesman problem along with the basic ACO and the proposed algorithm shows better performance than ACO algorithm  .