The sine-cosine algorithm (SCA) is a new population-based meta-heuristic algorithm. In addition to exploiting sine and cosine functions to perform local and global searches (hence the name sine-cosine), the SCA introduces several random and adaptive parameters to facilitate the search process. Although it shows promising results, the search process of the SCA is vulnerable to local minima maxima due to the adoption of a fixed switch probability and the bounded magnitude of the sine and cosine functions (from -1 to 1). In this paper, we propose a new hybrid Q-learning sine-cosine- based strategy, called the Q-learning sine-cosine algorithm (QLSCA). Within the QLSCA, we eliminate the switching probability. Instead, we rely on the Q-learning algorithm (based on the penalty and reward mechanism) to dynamically identify the best operation during runtime. Additionally, we integrate two new operations (Le´vy flight motion and crossover) into the QLSCA to facilitate jumping out of local minima maxima and enhance the solution diversity. To assess its performance, we adopt the QLSCA for the combinatorial test suite minimization problem. Experimental results reveal that the QLSCA is statistically superior with regard to test suite size reduction compared to recent state-of-the-art strategies, including the original SCA, the particle swarm test generator (PSTG), adaptive particle swarm optimization (APSO) and the cuckoo search strategy (CS) at the 95 confidence level. However, concerning the comparison with discrete particle swarm optimization (DPSO), there is no significant difference in performance at the 95 confidence level. On a positive note, the QLSCA statistically outperforms the DPSO in certain configurations at the 90 confidence level.
@cite_26 implemented a @math strategy based on ant colony optimization (ACO). The strategy simulates the behaviour of natural ant colonies in finding paths from the colony to the location of food. Each ant generates one candidate solution and walks through all paths in this solution by probabilistically choosing individual values. When the ant reaches the end of the last path, it returns and updates the initial candidate solution accordingly. This process continues until the iteration is complete. The final test case is chosen according to the maximum coverage of the t-interaction. Unlike the SA, the final test suite is further optimized by a merging algorithm that tries to merge the test cases.
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Interaction testing (also called combinatorial testing) is an cost-effective test generation technique in software testing. Most research work focuses on finding effective approaches to build optimal t-way interaction test suites. However, the strength of different factor sets may not be consistent due to the practical test requirements. To solve this problem, a variable strength combinatorial object and several approaches based on it have been proposed. These approaches include simulated annealing (SA) and greedy algorithms. SA starts with a large randomly generated test suite and then uses a binary search process to find the optimal solution. Although this approach often generates the minimal test suites, it is time consuming. Greedy algorithms avoid this shortcoming but the size of generated test suites is usually not as small as SA. In this paper, we propose a novel approach to generate variable strength interaction test suites (VSITs). In our approach, we adopt a one-test-at-a-time strategy to build final test suites. To generate a single test, we adopt ant colony system (ACS) strategy, an effective variant of ant colony optimization (ACO). In order to successfully adopt ACS, we formulize the solution space, the cost function and several heuristic settings in this framework. We also apply our approach to some typical inputs. Experimental results show the effectiveness of our approach especially compared to greedy algorithms and several existing tools. Swarm intelligence is a relatively new approach to problem solving that takes inspiration from the social behaviors of insects and of other animals. In particular, ants have inspired a number of methods and techniques among which the most studied and the most successful is the general purpose optimization technique known as ant colony optimization. Ant colony optimization (ACO) takes inspiration from the foraging behavior of some ant species. These ants deposit pheromone on the ground in order to mark some favorable path that should be followed by other members of the colony. Ant colony optimization exploits a similar mechanism for solving optimization problems. From the early nineties, when the first ant colony optimization algorithm was proposed, ACO attracted the attention of increasing numbers of researchers and many successful applications are now available. Moreover, a substantial corpus of theoretical results is becoming available that provides useful guidelines to researchers and practitioners in further applications of ACO. The goal of this article is to introduce ant colony optimization and to survey its most notable applications Ant Colony Optimization (ACO) is a new population oriented search metaphor that has been successfully applied to NP-hard combinatorial optimization problems. In this paper we discuss parallelization strategies for Ant Colony Optimization algorithms. We empirically test the most simple strategy, that of executing parallel independent runs of an algorithm. The empirical tests are performed applying MAX-MIN Ant System, one of the most efficient ACO algorithms, to the Traveling Salesman Problem and show that using parallel independent runs is very effective. This paper describes the basic concepts of the network migration problem, as well as its cost optimal solution with an ant colony algorithm. Generally there are many possible approaches to calculate network migration strategies. One of those is to consider the migration as a Shortest Path Problem, which can be solved using meta heuristics. In this paper it will be analyzed how ant colony optimization can be used to calculate migration sequences with minimized costs. Results of our investigation are information about the speed of convergence of ant colony algorithms towards an optimal cost value. The used resource utilization to cover increasing network demands will be presented also. Recently, a number of algorithms inspired by the foraging behavior of ant colonies have been applied to the solution of difficult discrete optimization problems. We put these algorithms in a common framework by defining the Ant Colony Optimization (ACO) meta-heuristic. A couple of paradigmatic examples of applications of these novel meta-heuristic are given, as well as a brief overview of existing applications. Many biological processes are governed by large assemblies of protein molecules. However, it is often very difficult to determine the three-dimensional structures of these assemblies using experimental biophysical techniques. Hence there is a need to develop computational approaches to fill this gap. This article presents an ant colony optimization approach to predict the structure of large multi-component protein complexes. Starting from pair-wise docking predictions, a multi-graph consisting of vertices representing the component proteins and edges representing candidate interactions is constructed. This allows the assembly problem to be expressed in terms of searching for a minimum weight spanning tree. However, because the problem remains highly combinatorial, the search space cannot be enumerated exhaustively and therefore heuristic optimisation techniques must be used. The utility of the ant colony based approach is demonstrated by re-assembling known protein complexes from the Protein Data Bank. The algorithm is able to identify near-native solutions for five of the six cases tested. This demonstrates that the ant colony approach provides a useful way to deal with the highly combinatorial multi-component protein assembly problem. Recently, many meta-heuristic algorithms have been proposed to serve as the basis of a t-way test generation strategy (where t indicates the interaction strength) including Genetic Algorithms (GA), Ant Colony Optimization (ACO), Simulated Annealing (SA), Cuckoo Search (CS), Particle Swarm Optimization (PSO), and Harmony Search (HS). Although useful, meta-heuristic algorithms that make up these strategies often require specific domain knowledge in order to allow effective tuning before good quality solutions can be obtained. Hyper-heuristics provide an alternative methodology to meta-heuristics which permit adaptive selection and or generation of meta-heuristics automatically during the search process. This paper describes our experience with four hyper-heuristic selection and acceptance mechanisms namely Exponential Monte Carlo with counter (EMCQ), Choice Function (CF), Improvement Selection Rules (ISR), and newly developed Fuzzy Inference Selection (FIS), using the t-way test generation problem as a case study. Based on the experimental results, we offer insights on why each strategy differs in terms of its performance. This paper presents a pseudo parallel ant colony optimization (ACO) algorithm in continuous domain. The variables of a solution are optimized by two parallel cooperative ACO-based processes, either of which attacks a relatively-independent sub-component of the original problem. Both processes contain tunable and untunable solution vectors. The best tunable vector migrates into the other process as an untunable vector through a migration controller, in which the migration strategy is synchronously sprung or adaptively controlled according to the temporal stagnation situation. Implementation of this mechanism is suitable for hardware which supports parallel computation, resulting in decline of unit computational cost and improvement of training speed. Optimization to a set of benchmark functions is carried out to prove the feasibility and efficiency of this parallel ACO algorithm. In this paper, we propose a novel method for image segmentation that we call ACS-MRF method. ACS-MRF is a hybrid ant colony system coupled with a local search. We show how a colony of cooperating ants are able to estimate the labels field and minimize the MAP estimate. Cooperation between ants is performed by exchanging information through pheromone updating. The obtained results show the efficiency of the new algorithm, which is able to compete with other stochastic optimization methods like Simulated annealing and Genetic algorithm in terms of solution quality. An algorithm of fine-grained parallel ant colony optimization algorithm(ACO) based on graphics process unit(GPU) accelerated is proposed to improve the performance of ACO for application to large-scale TSP problems.The process of parallel ACO is convert into that of parallel compute unified device architecture(CUDA) thread blocks,which makes PACO speed up.The experimental results show that the algorithm improves the ability of global search,increases the ant population in the PACO,speeds up its running and provides ordinary user with a feasible PACO solution. Wireless Sensor Networks consisting of nodes with limited power are deployed to gather useful information from the field. In WSNs it is critical to collect the information in an energy efficient manner. Ant Colony Optimization, a swarm intelligence based optimization technique, is widely used in network routing. A novel routing approach using an Ant Colony Optimization algorithm is proposed for Wireless Sensor Networks consisting of stable nodes. Illustrative examples, detailed descriptions and comparative performance test results of the proposed approach are included. The approach is also implemented to a small sized hardware component as a router chip. Simulation results show that proposed algorithm provides promising solutions allowing node designers to efficiently operate routing tasks. In this paper we introduce an Ant Colony Optimisation (ACO) algorithm for the Shortest Common Supersequence (SCS) problem, which has applications in production system planning, mechanical engineering and molecular biology. The ACO algorithm is used to find good parameters for a heuristic for the SCS problem. An island model with several populations of ants is used for the ACO algorithm. Besides we introduce a lookahead function which makes the decisions of the ants dependent on the state arrived after the decision. Pheromone trails laid by foraging ants serve as a positive feedback mechanism for the sharing of information about food sources. This feedback is nonlinear, in that ants do not react in a proportionate manner to the amount of pheromone deposited. Instead, strong trails elicit disproportionately stronger responses than weak trails. Such nonlinearity has important implications for how a colony distributes its workforce, when confronted with a choice of food sources. We investigated how colonies of the Pharaoh's ant, Monomorium pharaonis, distribute their workforce when offered a choice of two food sources of differing energetic value. By developing a nonlinear differential equation model of trail foraging, and comparing model with experiments, we examined how the ants allocate their workforce between the two food sources. In this allocation, the most profitable feeder (i.e. the feeder with the highest concentration of sugar syrup) was usually exploited by the majority of ants. The particular form of the nonlinear feedback in trail foraging means that when we offered the ants a choice between two feeders of equal profitability, foraging was biased to the feeder with the highest initial number of visitors. Taken together, our experiments illuminate how pheromones provide a mechanism whereby ants can efficiently allocate their workforce among the available food sources without centralized control. An adaptive parallel ant colony algorithm (PACO) is presented. In the algorithm, we propose a strategy for information exchange between processors which make each processor choose its partner to communicate and update the pheromone adaptively. We also propose a method of adjusting the time interval of information exchange adaptively according to the diversity of the solutions so as to increase the ability of search and avoid early convergence. Experimental results show that our algorithm PACO has high convergence speed, high speedup and efficiency. Ant colony optimization (ACO) is a metaheuristic approach to tackle hard combinatorial optimization problems. The basic component of ACO is a probabilistic solution construction mechanism. Due to its constructive nature, ACO can be regarded as a tree search method. Based on this observation, we hybridize the solution construction mechanism of ACO with beam search, which is a well-known tree search method. We call this approach Beam-ACO. The usefulness of Beam-ACO is demonstrated by its application to open shop scheduling (OSS). We experimentally show that Beam-ACO is a state-of-the-art method for OSS by comparing the obtained results to the best available methods on a wide range of benchmark instances. Data aggregation is important in energy constraint wireless sensor networks which exploits correlated sensing data and aggregates at the intermediate nodes to reduce the number of messages exchanged network. This paper considers the problem of constructing data aggregation tree in a wireless sensor network for a group of source nodes to send sensory data to a single sink node. The ant colony system provides a natural and intrinsic way of exploring search space in determining data aggregation. Moreover, we propose an ant colony algorithm for data aggregation in wireless sensor networks. Every ant will explore all possible paths from the source node to the sink node. The data aggregation tree is constructed by the accumulated pheromone. Simulations have shown that our algorithm can reduce significant energy costs. In this paper we present an application of ant colony optimization (ACO) to the Minimum Weighted Dominating Set Problem. We introduce a heuristic for this problem that takes into account the weights of vertexes being covered and show that it is more efficient than the greedy algorithm using the standard heuristic. Further we give implementation details of ACO applied to this problem. We tested our algorithm on graphs with different sizes, edge densities, and weight distribution functions and shown that it gives greatly improved results over these acquired by the greedy algorithms. An analogy with the way ant colonies function has suggested the definition of a new computational paradigm, which we call ant system (AS). We propose it as a viable new approach to stochastic combinatorial optimization. The main characteristics of this model are positive feedback, distributed computation, and the use of a constructive greedy heuristic. Positive feedback accounts for rapid discovery of good solutions, distributed computation avoids premature convergence, and the greedy heuristic helps find acceptable solutions in the early stages of the search process. We apply the proposed methodology to the classical traveling salesman problem (TSP), and report simulation results. We also discuss parameter selection and the early setups of the model, and compare it with tabu search and simulated annealing using TSP. To demonstrate the robustness of the approach, we show how the ant system (AS) can be applied to other optimization problems like the asymmetric traveling salesman, the quadratic assignment and the job-shop scheduling. Finally we discuss the salient characteristics-global data structure revision, distributed communication and probabilistic transitions of the AS. In this paper an ant colony optimization (ACO) algorithm for the minimum connected dominating set problem (MCDSP) is presented. The MCDSP become increasingly important in recent years due to its applicability to the mobile ad hoc networks (MANETs) and sensor grids. We have implemented a one-step ACO algorithm based on a known simple greedy algorithm that has a significant drawback of being easily trapped in local optima. We have shown that by adding a pheromone correction strategy and dedicating special attention to the initial condition of the ACO algorithm this negative effect can be avoided. Using this approach it is possible to achieve good results without using the complex two-step ACO algorithm previously developed. We have tested our method on standard benchmark data and shown that it is competitive to the existing algorithms. [Projekat Ministarstva nauke Republike Srbije, br. III-44006] According to recent research, the current Internet wastes energy due to an un-optimized network design, which does not consider the energy consumption of network elements such as routers and switches. Looking toward energy saving networks, a generalized problem called the energy consumption minimized network (EMN) had been proposed. However, due to the NP-completeness of this problem, it requires a considerable amount of time to obtain the solution, making it practically intractable for large-scale networks.In this paper, we re-formulate the NP-complete EMN problem into a simpler one using a newly defined concept called 'traffic centrality'. We then propose a new ant colony-based self-adaptive energy saving routing scheme, referred to as A-ESR, which exploits the ant colony optimization (ACO) method to make the Internet more energy efficient. The proposed A-ESR algorithm heuristically solves the re-formulated problem without any supervised control by allowing the incoming flows to be autonomously aggregated on specific heavily-loaded links and switching off the other lightly-loaded links. Additionally, the A-ESR algorithm adjusts the energy consumption by tuning the aggregation parameter s, which can dramatically reduce the energy consumption during nighttime hours (at the expense of tolerable network delay performance). Another promising capability of this algorithm is that it provides a high degree of self-organizing capabilities due to the amazing advantages of the swarm intelligence of artificial ants. The simulation results in real IP networks show that the proposed A-ESR algorithm performs better than previous algorithms in terms of its energy efficiency. The results also show that this efficiency can be adjusted by tuning s.