Showing papers on "Extremal optimization published in 2015"

A new hybrid method based on Particle Swarm Optimization, Ant Colony Optimization and 3-Opt algorithms for Traveling Salesman Problem

Abstract: The Traveling Salesman Problem (TSP) is one of the standard test problems used in performance analysis of discrete optimization algorithms. The Ant Colony Optimization (ACO) algorithm appears among heuristic algorithms used for solving discrete optimization problems. In this study, a new hybrid method is proposed to optimize parameters that affect performance of the ACO algorithm using Particle Swarm Optimization (PSO). In addition, 3-Opt heuristic method is added to proposed method in order to improve local solutions. The PSO algorithm is used for detecting optimum values of parameters α and β which are used for city selection operations in the ACO algorithm and determines significance of inter-city pheromone and distances. The 3-Opt algorithm is used for the purpose of improving city selection operations, which could not be improved due to falling in local minimums by the ACO algorithm. The performance of proposed hybrid method is investigated on ten different benchmark problems taken from literature and it is compared to the performance of some well-known algorithms. Experimental results show that the performance of proposed method by using fewer ants than the number of cities for the TSPs is better than the performance of compared methods in most cases in terms of solution quality and robustness.

Performance analysis of the multi-objective ant colony optimization algorithms for the traveling salesman problem

Abstract: Most real world combinatorial optimization problems are difficult to solve with multiple objectives which have to be optimized simultaneously. Over the last few years, researches have been proposed several ant colony optimization algorithms to solve multiple objectives. The aim of this paper is to review the recently proposed multi-objective ant colony optimization (MOACO) algorithms and compare their performances on two, three and four objectives with different numbers of ants and numbers of iterations. Moreover, a detailed analysis is performed for these MOACO algorithms by applying them on several multi-objective benchmark instances of the traveling salesman problem. The results of the analysis have shown that most of the considered MOACO algorithms obtained better performances for more than two objectives and their performance depends slightly on the number of objectives, number of iterations and number of ants used.

Random-key cuckoo search for the travelling salesman problem

Abstract: Combinatorial optimization problems are typically NP-hard, and thus very challenging to solve. In this paper, we present the random-key cuckoo search (RKCS) algorithm for solving the famous travelling salesman problem (TSP). We used a simplified random-key encoding scheme to pass from a continuous space (real numbers) to a combinatorial space. We also consider the displacement of a solution in both spaces using Levy flights. The performance of the proposed RKCS is tested against a set of benchmarks of symmetric TSP from the well-known TSPLIB library. The results of the tests show that RKCS is superior to some other metaheuristic algorithms.

Extremal Optimization applied to load balancing in execution of distributed programs

Abstract: The paper describes methods for using Extremal Optimization (EO) for processor load balancing during execution of distributed applications. A load balancing algorithm for clusters of multicore processors is presented and discussed. In this algorithm the EO approach is used to periodically detect the best tasks as candidates for migration and for a guided selection of the best computing nodes to receive the migrating tasks. To decrease the complexity of selection for migration, the embedded EO algorithm assumes a two-step stochastic selection during the solution improvement based on two separate fitness functions. The functions are based on specific models which estimate relations between the programs and the executive hardware. The proposed load balancing algorithm is assessed by experiments with simulated load balancing of distributed program graphs. The algorithm is compared against a greedy fully deterministic approach, a genetic algorithm and an EO-based algorithm with random placement of migrated tasks.

Tackling the Bi-criteria Facet of Multiple Traveling Salesman Problem with Ant Colony Systems

Abstract: The single-depot multiple TSP (SD-MTSP) is a simple extension of the standard TSP, in which more than one salesman is allowed to visit the set of interconnected cities, such that each city is visited exactly once (by a single salesman) and the total cost of the traveled subtours is minimized. Although Ant Colony Systems (ACSs) are a natural choice for shortest-path problems, with TSP at its core, the application of ACS on this straightforward extension is not properly explored. The reasons may lie in the bi-criteria nature of the problem (shortest cost versus balanced subtours) and the lack of dedicated benchmarks exposing optimal solutions. This paper attempts at proposing and evaluating from a bi-criteria perspective several multi-objective ACSs to tackle SD-MTSP when two objectives need to be simultaneously optimized: minimizing the total cost of traveled subtours while achieving balanced subtours. Experiments are conducted towards investigating the efficiency of the algorithms in a multi-objective setting.

Ant Colony Optimization: A Tutorial Review

Abstract: The complex social behaviors of ants have been much studied, and now scientists are finding that these behavior patterns can provide models for solving difficult combinatorial optimization problems. The attempt to develop algorithms inspired by one aspect of ant behavior, the ability to find shortest paths, has become the field of ant colony optimization (ACO). Ant Colony Optimization (ACO) is a derivative of Swarm intelligence (SI). The ant colony optimization algorithm (ACO), introduced by Marco Dorigo, in the year 1992 and it is a paradigm for designing meta heuristic algorithms for optimization problems and is inspired by the foraging behavior of ant colonies. Ant Colony Optimization targets discrete optimization problems and can be extended to continuous optimization problems which is useful to find approximate solutions. Now-a-days, a number of algorithms inspired by the foraging behavior of ant colonies have been applied to solve difficult discrete optimization problems. In fact, ACO algorithm is the most successful and widely recognized algorithm based on the ant behavior. This paper gives an overview of growing research field from theoretical inception to the practical applications of ACO variants and some of the fields where it can be applied.

An Ant Colony Optimization Based Memetic Algorithm for the Dynamic Travelling Salesman Problem

Abstract: Ant colony optimization (ACO) algorithms have proved to be able to adapt for solving dynamic optimization problems (DOPs). The integration of local search algorithms has also proved to significantly improve the output of ACO algorithms. However, almost all previous works consider stationary environments. In this paper, the MAX -MIN Ant System, one of the best ACO variations, is integrated with the unstringing and stringing (US) local search operator for the dynamic travelling salesman problem (DTSP). The best solution constructed by ACO is passed to the US operator for local search improvements. The proposed memetic algorithm aims to combine the adaptation capabilities of ACO for DOPs and the superior performance of the US operator on the static travelling salesman problem in order to tackle the DTSP. The experiments show that the MAX -MIN Ant System is able to provide good initial solutions to US and the proposed algorithm outperforms other peer ACO-based memetic algorithms on different DTSPs.

An Improved Shuffled Frog-Leaping Algorithm for Flexible Job Shop Scheduling Problem

Abstract: The flexible job shop scheduling problem is a well-known combinatorial optimization problem. This paper proposes an improved shuffled frog-leaping algorithm to solve the flexible job shop scheduling problem. The algorithm possesses an adjustment sequence to design the strategy of local searching and an extremal optimization in information exchange. The computational result shows that the proposed algorithm has a powerful search capability in solving the flexible job shop scheduling problem compared with other heuristic algorithms, such as the genetic algorithm, tabu search and ant colony optimization. Moreover, the results also show that the improved strategies could improve the performance of the algorithm effectively.

Discrete cuttlefish optimization algorithm to solve the travelling salesman problem

Abstract: The cuttlefish optimization algorithm is a new combinatorial optimization algorithm in the family of metaheuristics, applied in the continuous domain, and which provides mechanisms for local and global research. This paper presents a new adaptation of this algorithm in the discrete case, solving the famous travelling salesman problem, which is one of the discrete combinatorial optimization problems. This new adaptation proposes a reformulation of the equations to generate solutions depending a different algorithm cases. The experimental results of the proposed algorithm on instances of TSPLib library are compared with the other methods, show the efficiency and quality of this adaptation.

Parallel Performance of an Ant Colony Optimization Algorithm for TSP

Abstract: MAX-MIN ant colony system (MMAS) has been one of the most effective ant colony optimization algorithm for the traveling salesman problem (TSP) up to the present. Despite the intrinsic parallelism, problems such as excessive memory occupation and overlong communication cost arise in the parallel process for large-scale numerical examples. In this paper, for parallel optimization of MMAS, some strategies are proposed: a) by comparing the current solution with the optimal solution, unnecessary ergodic paths and iterations are abandoned, which accelerates the searching process, b) for generating distance matrix and updating pheromone matrix, communications are replaced by calculations, which reduces memory occupation and communication cost enormously. MMAS with the above strategies is implemented on the Sunway Blue Light supercomputer based on MPI. As a result, high feasibility and effectiveness are verified.

Cooperative co-evolutionary algorithms for large-scale optimization

Abstract: The aim of this research is to investigate the use of a divide-and-conquer approach for solving continuous large-scale global optimization problems using evolutionary methods. The curse of dimensionality is a major hindrance to the efficient optimization

Comparison and analysis of solving travelling salesman problem using GA, ACO and hybrid of ACO with GA and CS

Abstract: The Travelling Salesman Problem (TSP) is a very popular combinatorial optimization problem of real world. The objective is to find out a shortest possible path travelled by a salesman while visited every city once and returned to the origin city. TSP is one of the NP hard problems and several attempts have been done to solve it by traditional methods. Computational methods give better solution for TSP as most of them are based on repetitive learning. In the proposed paper four optimization techniques are presented such as ant colony optimization (ACO), genetic algorithm (GA), hybrid technique of ant colony optimization (ACO) and genetic algorithm (GA) and hybrid technique of ant colony optimization (ACO) and cuckoo search (CS) algorithm is proposed and implemented for travelling salesman problem. The result shows that shortest efficient tour is obtained by new hybrid algorithm.

A Non-hybrid Ant Colony Optimization Heuristic for Convergence Quality

Abstract: Ant Colony Optimization has proven to be an important optimization technique. It has provided a solid base for solving classical computational problems, networks routing problems and many others. Nonetheless, algorithms within the Ant Colony metaheuristic have been shown to struggle to reach the global optimum of the search space, with only a few select ones guaranteed to reach it at all. On the other hand, Ant Colony based hybrid solutions that address this issue suffer from either severely decreased efficiency or low scalability and are usually static and custom-made, with only one particular use. In this paper we present a generic and robust solution to this problem, restricted rigorously to the Ant Colony Optimization paradigm, named Angry Ant Framework. It adds a new dimension -- a dynamic, biologically-inspired pheromone stratification, which we hope can become the objective of further state-of-the-art research. We present a series of experiments to enable a discussion on the benefits provided by this new framework. In particular, we show that Angry Ant Framework increases the efficiency, while at the same time improving the flexibility, the adaptability and the scalability with a very low computational investment.

A knowledge-based initialization technique of genetic algorithm for the travelling salesman problem

Abstract: Genetic Algorithm (GA) is efficient for the travelling salesman problem, but it has the defect of slow convergence and is easily trapped in local optima. Because the initialization has a profound impact on the optimization, this study proposed to improve the performance of GA by applying a knowledge-based initialization technique (KI). KI learns the features of evolved population and uses them to guide the generation of initial population. Advanced initial solution without path crossover can be fast generated with this method. Instances in TSPLIB were used to test different initialization methods. The results proved that this proposed technique helped GA get better initial population and performance.

Improving ant colony optimization performance through prediction of best termination condition

Abstract: The Ant Colony System (ACS) is a well-known bio-inspired optimization algorithm which has been successfully applied to several NP-hard optimization problems, including transportation network optimization. This paper introduces a method to improve the computational time required by the algorithm in finding high quality solutions. The purpose of the method is to predict the best termination iteration for an unseen instance by analyzing the performance of the optimization process on solved instances. A fitness landscape analysis is used to understand the behavior of the optimizer on all given instances. A comprehensive set of features is presented to characterize instances of the transportation network optimization problem. This set of features is associated to the results of the fitness landscape analysis through a machine learning-based approach, so that the behavior of the optimization algorithm may be predicted before the optimization start and the termination iteration may be set accordingly. The proposed system has been tested on a real-world transportation network optimization problem and two randomly generated problems. The proposed method has drastically reduced the computational times required by the ACS in finding high quality solutions.

Mixing Network Extremal Optimization for Community Structure Detection

Abstract: Mixing Network Extremal Optimization is a new algorithm designed to identify the community structure in networks by using a game theoretic approach and a network mixing mechanism as a diversity preserving method. Numerical experiments performed on synthetic and real networks illustrate the potential of the approach.

A novel hybrid penguins search optimization algorithm to solve travelling salesman problem

Abstract: Metaheuristics form a family of optimization algorithms for solving combinatorial optimization problems by applying the research procedures to quickly find a good approximation of the best solution. In this paper we proposed a new metaheuristic novel hybrid penguins search optimization algorithm (NPeSOA) which is based on the combination of penguins search optimization algorithm (PeSOA) and harmony search algorithm (HS) to solve the Travelling Salesman Problem. The search for harmony was added to improve the research technique of PeSOA method. The results of this experience are tested by the instances of TSPLib, and compared with the methods of PeSOA and HS to show the efficiency of NPeSOA.

An evolutionary algorithm based on decomposition for multimodal optimization problems

Abstract: This paper presents a non-parameter method to identify the peaks of the multi-modal optimization problems provided that the peaks are characterized by a smaller objective values than their neighbors and by a relatively large distance from points with smaller objective value. Using the identified peaks as the seeds, we decompose the population into some subpopulations and dynamically allocate the computational effort to different subpopulations. We evaluate the proposed approach on the CEC2015 single objective multi-niche optimization problems. The promising experimental results show its efficacy.

Application of ant colony optimization algorithms for transportation problems using the example of the travelling salesman problem

Abstract: This thesis presents the travelling salesman problem and the application of heuristics in ant colony optimization algorithms. The thesis also discusses the results of an experiment carried out to solve the travelling salesman problem using the ant colony system with different heuristics. An example is focused on heuristics application and comparison.

Ant Colony Optimization for Solving the Quadratic Assignment Problem

Abstract: Many real-world problems in logistics, transport, and manufacturing can be modeled as combinatorial optimization problems. In this work, a hybrid variant of meta-heuristic algorithm ant colony optimization (ACO) is used. Different variants of ant colony optimization have been applied to the quadratic assignment problem (QAP). In this paper a hybrid approach is proposed, which is combination of Ant system and other meta-heuristic approaches to take benefits of both methods. This hybrid approach is accompanied by a local search technique. Moreover, a comparative analysis is done using QAPLIB.

Combinatorial Optimization of Spray Painting Robot Tool Trajectory Based on Improved Membership Cloud Models Ant Colony Algorithm

Abstract: Aimed at the combinatorial optimization of tool trajectory upon the complex free-form curved surface piece,Hamiltonian path was adopted to transform it to an open traveling salesman problem(OTSP).Meanwhile the strategy of integrating the problem-unrelated optimization algorithm and the problem-related local search was adopted.First,the membership cloud models were introduced to adapt and adjust the randomness controlled by the ant colony algorithm.Then,the K-opt partial search strategy was introduced to find the solution to OTSP in respect of the combinatorial optimization of the tool trajectory based on the improved membership cloud models ant colony algorithm(MCMACA).The simulation result shows that MCMACA features better global search ability and local convergence.Meanwhile,it has obvious advantages in terms of optimization of the spray painting robot tool trajectory on complex curved surface.