Reactive approach for automating exploration and exploitation in ant colony optimization

Ant colony optimization (ACO) algorithms can be used to solve nondeterministic polynomial hard problems. Exploration and exploitation are the main mechanisms in controlling search within the ACO. Reactive search is an alternative technique to maintain the dynamism of the mechanics. However, ACO-based reactive search technique has three (3) problems. First, the memory model to record previous search regions did not completely transfer the neighborhood structures to the next iteration which leads to arbitrary restart and premature local search. Secondly, the exploration indicator is not robust due to the difference of magnitudes in distance matrices for the current population. Thirdly, the parameter control techniques that utilize exploration indicators in their feedback process do not consider the problem of indicator robustness. A reactive ant colony optimization (RACO) algorithm has been proposed to overcome the limitations of the reactive search. RACO consists of three main components. The first component is a reactive max-min ant system algorithm for recording the neighborhood structures. The second component is a statistical machine learning mechanism named ACOustic to produce a robust exploration indicator. The third component is the ACO-based adaptive parameter selection algorithm to solve the parameterization problem which relies on quality, exploration and unified criteria in assigning rewards to promising parameters. The performance of RACO is evaluated on traveling salesman and quadratic assignment problems and compared with eight metaheuristics techniques in terms of success rate, Wilcoxon signed-rank, Chi-square and relative percentage deviation. Experimental results showed that the performance of RACO is superior than the eight (8) metaheuristics techniques which confirmed that RACO can be used as a new direction for solving optimization problems. RACO can be used in providing a dynamic exploration and exploitation mechanism, setting a parameter value which allows an efficient search, describing the amount of exploration an ACO algorithm performs and detecting stagnation situations

Hybrid bat-ant colony optimization algorithm for rule-based feature selection in health care

Rule-based classification in the field of health care using artificial intelligence provides solutions in decision-making problems involving different domains. An important challenge is providing access to good and fast health facilities. Cervical cancer is one of the most frequent causes of death in females. The diagnostic methods for cervical cancer used in health centers are costly and time-consuming. In this paper, bat algorithm for feature selection and ant colony optimization-based classification algorithm were applied on cervical cancer data set obtained from the repository of the University of California, Irvine to analyze the disease based on optimal features. The proposed algorithm outperforms other methods in terms of comprehensibility and obtains better results in terms of classification accuracy

ACOustic: A nature-inspired exploration indicator for ant colony optimization

A statistical machine learning indicator, ACOustic, is proposed to evaluate the exploration behavior in the iterations of ant colony optimization algorithms. This idea is inspired by the behavior of some parasites in their mimicry to the queens’ acoustics of their ant hosts.The parasites’ reaction results from their ability to indicate the state of penetration.The proposed indicator solves the problem of robustness that results from the difference of magnitudes in the distance’s matrix, especially when combinatorial optimization problems with rugged fitness landscape are applied.The performance of the proposed indicator is evaluated against the existing indicators in six variants of ant colony optimization algorithms.Instances for travelling salesman problem and quadratic assignment problem are used in the experimental evaluation.The analytical results showed that the proposed indicator is more informative and more robust

Reactive max-min ant system: An experimental analysis of the combination with K-OPT local searches

Ant colony optimization (ACO) is a stochastic search method for solving NP-hard problems. The exploration versus exploitation dilemma rises in ACO search.Reactive max-min ant system algorithm is a recent proposition to automate the exploration and exploitation.It memorizes the search regions in terms of reactive heuristics to be harnessed after restart, which is to avoid the arbitrary exploration later.This paper examined the assumption that local heuristics are useless when combined with local search especially when it applied for combinatorial optimization problems with rugged fitness landscape.Results showed that coupling reactive heuristics with k-Opt local search algorithms produces higher quality solutions and more robust search than max-min ant system algorithm.Well-known combinatorial optimization problems are used in experiments, i.e. traveling salesman and quadratic assignment problems. The benchmarking data for both problems are taken from TSPLIB and QAPLIB respectively

Adaptive Parameter Control Strategy for Ant-Miner Classification Algorithm

Pruning is the popular framework for preventing the dilemma of overfitting noisy data. This paper presents a new hybrid Ant-Miner classification algorithm and ant colony system (ACS), called ACS-AntMiner. A key aspect of this algorithm is the selection of an appropriate number of terms to be included in the classification rule. ACS-AntMiner introduces a new parameter called importance rate (IR) which is a pre-pruning criterion based on the probability (heuristic and pheromone) amount. This criterion is responsible for adding only the important terms to each rule, thus discarding noisy data. The ACS algorithm is designed to optimize the IR parameter during the learning process of the Ant-Miner algorithm. The performance of the proposed classifier is compared with related ant-mining classifiers, namely, Ant-Miner, CAnt-Miner, TACO-Miner, and Ant-Miner with a hybrid pruner across several datasets. Experimental results show that the proposed classifier significantly outperforms the other ant-mining classifiers

Modified ACS centroid memory for data clustering

Ant Colony Optimization (ACO) is a generic algorithm, which has been widely used in different application domains due to its simplicity and adaptiveness to different optimization problems. The key component that governs the search process in this algorithm is the management of its memory model. In contrast to other algorithms, ACO explicitly utilizes an adaptive memory, which is important to its performance in terms of producing optimal results. The algorithm’s memory records previous search regions and is fully responsible for transferring the neighborhood of the current structures to the next iteration. Ant Colony Optimization for Clustering (ACOC) is a swarm algorithm inspired from nature to solve clustering issues as optimization problems. However, ACOC defined implicit memory (pheromone matrix) inability to retain previous information on an ant’s movements in the pheromone matrix. The problem arises because ACOC is a centroid-label clustering algorithm, in which the relationship between a centroid and instance is unstable. The label of the current centroid value changes from one iteration to another because of changes in centroid label. Thus the pheromone values are lost because they are associated with the label (position) of the centroid. ACOC cannot transfer the current clustering solution to the next iterations due to the history of the search being lost during the algorithm run. This study proposes a new centroid memory (A-ACOC) for data clustering that can retain the information of a previous clustering solution. This is possible because the pheromone is associated with the adaptive instance and not with label of the centroid. Centroids will be identified based on the adaptive instance route. A comparison of the performance of several common clustering algorithms using real-world data sets shows that the accuracy of the proposed algorithm surpasses those of its counterparts

Unified strategy for intensification and diversification balance in ACO metaheuristic

This intensification and diversification in Ant Colony Optimization (ACO) is the search strategy to achieve a trade-off between learning a new search experience (exploration) and earning from the previous experience (exploitation).The automation between the two processes is maintained using reactive search. However, existing works in ACO were limited either to the management of pheromone memory or to the adaptation of few parameters.This paper introduces the reactive ant colony optimization (RACO) strategy that sticks to the reactive way of automation using memory, diversity indication, and parameterization. The performance of RACO is evaluated on the travelling salesman and quadratic assignment problems from TSPLIB and QAPLIB, respectively.Results based on a comparison of relative percentage deviation revealed the superiority of RACO over other well-known metaheuristics algorithms.The output of this study can improve the quality of solutions as exemplified by RACO

Ant-based sorting and ACO-based clustering approaches: A review

Data clustering is used in a number of fields including statistics, bioinformatics, machine learning exploratory data analysis, image segmentation, security, medical image analysis, web handling and mathematical programming.Its role is to group data into clusters with high similarity within clusters and with high dissimilarity between clusters.This paper reviews the problems that affect clustering performance for deterministic clustering and stochastic clustering approaches.In deterministic clustering, the problems are caused by sensitivity to the number of provided clusters.In stochastic clustering, problems are caused either by the absence of an optimal number of clusters or by the projection of data.The review is focused on ant-based sorting and ACO-based clustering which have problems of slow convergence, un-robust results and local optima solution.The results from this review can be used as a guide for researchers working in the area of data clustering as it shows the strengths and weaknesses of using both clustering approaches

An improved ACS algorithm for data clustering

Data clustering is a data mining technique that discovers hidden patterns by creating groups (clusters) of objects. Each object in every cluster exhibits sufficient similarity to its neighbourhood, whereas objects with insufficient similarity are found in other clusters. Data clustering techniques minimise intra-cluster similarity in each cluster and maximise inter-cluster dissimilarity amongst different clusters. Ant colony optimisation for clustering (ACOC) is a swarm algorithm inspired by the foraging behaviour of ants. This algorithm minimises deterministic imperfections in which clustering is considered an optimisation problem. However, ACOC suffers from high diversification in which the algorithm cannot search for best solutions in the local neighbourhood. To improve the ACOC, this study proposes a modified ACOC, called M-ACOC, which has a modification rate parameter that controls the convergence of the algorithm. Comparison of the performance of several common clustering algorithms using real-world datasets shows that the accuracy results of the proposed algorithm surpasses other algorithms

Balancing exploration and exploitation in ACS algorithms for data clustering

Ant colony optimization (ACO) is a swarm algorithm inspired by different behaviors of ants. The algorithm minimizes deterministic imperfections by assuming the clustering problem as an optimization problem. A balanced exploration and exploitation activity is necessary to produce optimal results. ACO for clustering (ACOC) is an ant colony system (ACS) algorithm inspired by the foraging behavior of ants for clustering tasks. The ACOC performs clustering based on random initial centroids, which are generated iteratively during the algorithm run. This makes the algorithm deviate from the clustering solution and performs a biased exploration. This study proposes a modified ACOC called the population ACOC (P-ACOC) to address this issue. The proposed P-ACOC allows the ants to process and update their own centroid during the algorithm run, thereby intensifying the search at the neighborhood before moving to another location.However, the algorithm quickly produces a premature convergence due to the exploitation of the same clustering results during centroid update. To resolve this issue, this study proposes a second modification by adding a restart strategy that balances between the exploration and exploitation strategy in P-ACOC.Each time the algorithm begins to converge with the same clustering solution, the restart strategy is performed to change the behavior of the algorithm from exploitation to exploration. The performance of the proposed algorithm is compared with that of several common clustering algorithms using real-world datasets. The results show that the accuracy of the proposed algorithm surpasses those of other algorithms