Ant Colony Optimization Variants Research Articles

An Adaptive Convergence-Trajectory Controlled Ant Colony Optimization Algorithm With Application to Water Distribution System Design Problems

Evolutionary algorithms and other meta-heuristics have been employed widely to solve optimization problems in many different fields over the past few decades. Their performance in finding optimal solutions often depends heavily on the parameterization of the algorithm’s search operators, which affect an algorithm’s balance between search diversification and intensification. While many parameter-adaptive algorithms have been developed to improve the searching ability of meta-heuristics, their performance is often unsatisfactory when applied to real-world problems. This is, at least in part, because available computational budgets are often constrained in such settings due to the long simulation times associated with objective function and/or constraint evaluation, thereby preventing convergence of existing parameter-adaptive algorithms. To this end, this paper proposes an innovative parameter-adaptive strategy for ant colony optimization (ACO) algorithms based on controlling the convergence trajectory in decision space to follow any prespecified path, aimed at finding the best possible solution within a given, and limited, computational budget. The utility of the proposed convergence-trajectory controlled ACO (ACO $_{\mathbf{CTC}}$ ) algorithm is demonstrated using six water distribution system design problems (WDSDPs, a difficult type of combinatorial problem in water resources) with varying complexity. The results show that the proposed ACO $_{\mathbf{CTC}}$ successfully enables the specified convergence trajectories to be followed by automatically adjusting the algorithm’s parameter values. Different convergence trajectories significantly affect the algorithm’s final performance (solution quality). The trajectory with a slight bias toward diversification in the first half and more emphasis on intensification during the second half of the search exhibits substantially improved performance compared to the best available ACO variant with the best parameterization (no convergence control) for all WDSDPs and computational scenarios considered. For the two large-scale WDSDPs, new best-known solutions are found by the proposed ACO $_{\mathbf{CTC}}$ .

A new ants interaction scheme for continuous optimization problems

Ant colony optimization (ACO) algorithms have been used successfully to solve a wide variety of combinatorial optimization problems. In the recent past many modifications have been proposed in ACO algorithms to solve continuous optimization problems. However, most of the ACO variants to solve continuous optimization problems lack ability of efficient exploration of the search space and suffer from the problem of premature convergence. In this work a new ACO algorithm (ACO–LD) is proposed that incorporates Laplace distribution based interaction scheme among the ants. Also, in order to avoid the problem of stagnation, an additional diversification mechanism is introduced. The proposed ACO–LD is tested on benchmark test functions taken from Congress on Evolutionary Computation 2014 (CEC2014) and the results are compared with four state-of-the-art algorithms reported in CEC2014. ACO–LD is also applied to solve six real life problems and the results are compared with the results of six other algorithms reported in the literature. The analysis of the results shows that the overall performance of ACO–LD is found to be better than the other algorithms included in the present study.

A hybrid genetic-ant colony optimization algorithm for the word sense disambiguation problem

Word sense disambiguation (WSD) is a natural language processing problem that occurs at the semantic level. It consists of determining the sense of a polysemous word that is suitable in a particular context. WSD has been addressed using several approaches, including metaheuristic algorithms. We propose hybrid algorithms for WSD that consist of a self-adaptive genetic algorithm (SAGA) and variants of ant colony optimization (ACO) algorithms: max-min ant system (MMAS) and ant colony system (ACS). SAGA is used to automatically tune the parameters of MMAS and ACS. The ACO algorithms are adapted based on a combination of semantic relatedness between sequences of senses corresponding to the context words and semantic relatedness between the sense of a target word and the sense of a context word. We evaluated the performance of the two ACO algorithms (MMASWSD and ACSWSD) and their hybridization with SAGA (GMMASWSD and GACSWSD) on fine-grained and coarse-grained corpora, and compared them with the best-performing algorithms. The empirical results indicate that GMMASWSD outperformed the other variants and all of the rival algorithms on the fine-grained corpora. However, GMMASWSD did not achieve the best performance on the coarse-grained corpus, even though its performance was close to that of the best algorithm.

A modified ant system to achieve better balance between intensification and diversification for the traveling salesman problem

This paper presents a new variant of Ant Colony Optimization (ACO) for the Traveling Salesman Problem (TSP). ACO has been successfully used in many combinatorial optimization problems. However, ACO has a problem in reaching the global optimal solutions for TSPs, and the algorithmic performance of ACO tends to deteriorate significantly as the problem size increases. In the proposed modification, adaptive tour construction and pheromone updating strategies are embedded into the conventional Ant System (AS), to achieve better balance between intensification and diversification in the search process. The performance of the proposed algorithm is tested on randomly generated data and well-known existing data. The computational results indicate the proposed modification is effective and efficient for the TSP and competitive with Ant Colony System (ACS), Max-Min Ant System (MMAS), and Artificial Bee Colony (ABC) Meta-Heuristic.

Effectiveness of Ant Colony Optimization with Hybrid distance for document Retrieval

In the context of document ranking the user’s information requirement is often expressed partially as a query consisting of a set of keywords. The existing distance estimates though proven good for comparing two documents expressed as n-tuples, are not effective while comparing a query expressed as k-tuple (k<<n) with a document expressed as n-tuple. This proposed method deals with these two challenges by using parametric modelling for identifying plausibly relevant documents based on the query expressing the information requirement partially. This task relevant subset of documents is intensely explored to cluster them into semantically related groups. The author proposed to estimate the similarity between a pair of documents by integrating the results of micro as well as macro level analysis. A new Hybrid Distance metric is devised as a combination of frequency based distance (Macro level) and Structure based distance (Micro level). The hybrid distance is used for clustering the documents employing a scalable variant of Ant Colony Optimization that simulates the brood sorting behavior of ants with two modifications. The author proposed to use winged ants that hop instead of walk on the 2D grid where each grid cell accommodates multiple documents instead of single document to form better clusters in less number of iterations.Each Document cluster represents semantically related group of documents on a theme(s) and hence contain relevant documents for a particular information requirement based on its resemblance to the query. Documents of most resembling cluster are ranked based on their resemblance to form document ranking in response to the user’s query. The performance of clustering and document ranking has been tested on standard data repository which is a subset of Wikipedia using F-measure and Mean Average Precision respectively.

Predicting Flood in Perlis Using Ant Colony Optimization

Flood forecasting is widely being studied in order to reduce the effect of flood such as loss of property, loss of life and contamination of water supply. Usually flood occurs due to continuous heavy rainfall. This study used a variant of Ant Colony Optimization (ACO) algorithm named the Ant-Miner to develop the classification prediction model to predict flood. However, since Ant-Miner only accept discrete data, while rainfall data is a time series data, a pre-processing steps is needed to discretize the rainfall data initially. This study used a technique called the Symbolic Aggregate Approximation (SAX) to convert the rainfall time series data into discrete data. As an addition, Simple K-Means algorithm was used to cluster the data produced by SAX. The findings show that the predictive accuracy of the classification prediction model is more than 80%.

A Non-dominated Sorting Ant Colony Optimization Algorithm Approach to the Bi-objective Multi-vehicle Allocation of Customers to Distribution Centers

Distribution centers (DCs) play important role in maintaining the uninterrupted flow of goods and materials between the manufacturers and their customers.This paper proposes a mathematical model as the bi-objective capacitated multi-vehicle allocation of customers to distribution centers. An evolutionary algorithm named non-dominated sorting ant colony optimization (NSACO) is used as the optimization tool for solving this problem. The proposed methodology is based on a new variant of ant colony optimization (ACO) specialized in multi-objective optimization problem. To aid the decision maker choosing the best compromise solution from the Pareto front, the fuzzy-based mechanism is employed for this purpose. For ensuring the robustness of the proposed method and giving a practical sense of this study, the computational results are compared with those obtained by NSGA-II algorithm. Results show that both NSACO and NSGA-II algorithms can yield an acceptable number of non-dominated solutions. In addition, the results show while the distribution of solutions in the trade-off surface of both NSACO and NSGA-II algorithms do not differ significantly, the computational CPU time of NSACO is considerably lower than that of NSGA-II. Moreover, it can be seen that the fast NSACO algorithm is more efficient than NSGA-II in the viewpoint of the optimality and convergence.

Variants of Ant Colony Optimization- A State of an Art

Ant Colony Optimization, a Swarm intelligence method which solves NP hard problems inspired from the behaviour of ant foraging (Searching for food) the heuristics they use and partial guidance by other ants in indirect format (Stigmergy). This paper developed to list out the variations of the ACO application, its variants with clear diagrammatic and graphical representation. Clear denotation of the techniques they have used to solve such problems, representation of problems, transformations, parameters used and advantage of the techniques and used variants. Through this article we identify some open suggestions with a certain interest of being solved in near future. Keywords: Heuristics, Pheromone, Stigmergy

Analyzing the Effects of Instance Features and Algorithm Parameters for Max–Min Ant System and the Traveling Salesperson Problem

Ant colony optimization (ACO) performs very well on many hard optimization problems, even though no good worst case guarantee can be given. Understanding the effects of different ACO parameters and the structural features of the considered problem on algorithm performance has become an interesting problem. In this paper, we study structural features of easy and hard instances of the Traveling Salesperson problem for a well-known ACO variant called Max Min Ant System MMAS) for several parameter settings. The four considered parameters are the importance of pheromone values, the heuristic information, the pheromone update strength and the number of ants. We further use this knowledge to predict the best parameter setting for a wide range of instances taken from TSPLIB.

Ant colony optimization on a limited budget of evaluations

Ant colony optimization (ACO) is a successful method for solving difficult combinatorial optimization problems. Following Ant System, the first ACO algorithm, a large number of algorithmic variants have been developed that showed significantly better performance on a wide range of optimization problems. Typically, performance was measured according to the solution quality achieved for a given computation time limit, which usually allowed the evaluation of a very large number of candidate solutions, often in the range of millions. However, there are practical applications where the number of evaluations that can be done is very restricted due to tight real-time constraints or to the high computational cost of evaluating a solution. Since these situations are quite different from those for which ACO algorithms were initially designed, current knowledge on good parameter settings or the most promising search strategies may not be directly applicable. In this paper, we examine the performance of different ACO algorithms under a strongly limited budget of 1000 evaluations. We do so using default parameter settings from the literature and parameter settings tuned for the limited-budget scenario. In addition, we compare the performance of the ACO algorithms to algorithms that make use of surrogate modeling of the search landscapes. We show that tuning algorithms for the limited-budget case is of uttermost importance, that direct search through the ACO algorithms keeps an edge over techniques using surrogate modeling, and that the ACO variants proposed as improvements over Ant System remain preferable.

An alternative solution for the repair of electrical breakdowns after natural disasters based on ant colony optimization

Abundant literature is available for the route planning based on meta-heuristic algorithms. However, most researches in this field are developed under normal scenarios (e.g. normal weather conditions). The natural disasters, such as hurricanes, on the contrary, impose hard constraints to these combinatorial problems. In this paper, a route-planning problem is solved, specifically, for the repair of electrical breakdowns that occur after natural disasters. The problem is modeled using an assignment-based integer programming formulation proposed for the Multiple Traveling Salesman Problem (mTSP). Moreover, this paper proposes the creative application of an algorithm based on Ant Colony Optimization (ACO), specifically Multi-type Ant Colony System (M-ACS), where each colony represents a set of possible global solutions. Ants cooperate and compete by means of “frequent” pheromone exchanges aimed to find a solution. The algorithm performance has been compared against other ACO variant, showing the efficacy of the proposed algorithm on realistic decision-making.

Long term production planning of open pit mines by ant colony optimization

The problem of long-term production planning of open pit mines is a large combinatorial problem. Application of mathematical programming approaches suffer from reduced computational efficiency due to the large amount of decision variables. This paper presents a new metaheuristic approximation approach based on the Ant Colony Optimization (ACO) for the solution of the problem of open-pit mine production planning. It is a three-dimensional optimization procedure which has the capability of considering any type of objective function, non-linear constraints and real technical restrictions. The proposed process is programmed and tested through its application on a real scale Copper–Gold deposit. The study revealed that the ACO approach is capable to improve the value of the initial mining schedule regarding the current commercial tools considering penalties and without, in a reasonable computational time. Several variants of ACO were examined to find the most compatible variants and the best parameter ranges. Results indicated that the Max–Min Ant System (MMAS) and the Ant Colony System (ACS) are the best possible variants based on the required less amount of memory. It is also proved that the MMAS is the most explorative variant, while the ACS is the fastest method.

Enriched ant colony optimization and its application in feature selection

This paper presents a new variant of ant colony optimization (ACO), called enRiched Ant Colony Optimization (RACO). This variation tries to consider the previously traversed edges in the earlier executions to adjust the pheromone values appropriately and prevent premature convergence. Feature selection (FS) is the task of selecting relevant features or disregarding irrelevant features from data. In order to show the efficacy of the proposed algorithm, RACO is then applied to the feature selection problem. In the RACO-based feature selection (RACOFS) algorithm, it might be assumed that the proposed algorithm considers later features with a higher priority. Hence in another variation, the algorithm is integrated with a capability local search procedure to demonstrate that this is not the case. The modified RACO algorithm is able to find globally optimal solutions but suffers from entrapment in local optima. Hence, in the third variation, the algorithm is integrated with a local search procedure to tackle this problem by searching the vicinity of the globally optimal solution. To demonstrate the effectiveness of the proposed algorithms, experiments were conducted using two measures, kappa statistics and classification accuracy, on several standard datasets. The comparisons were made with a wide variety of other swarm-based algorithms and other feature selection methods. The results indicate that the proposed algorithms have superiorities over competitors.

Ant Colony System with Heuristic Function for the Travelling Salesman Problem

Abstract Ant colony system which is classified as a meta-heuristic algorithm is considered as one of the best optimization algorithm for solving different type of NP-Hard problem including the travelling salesman problem. A heuristic function in the Ant colony system uses pheromone and distance values to produce heuristic values in solving the travelling salesman problem. However, the heuristic values are not updated in the entire process to reflect the knowledge discovered by ants while moving from city to city. This paper presents the work on enhancing the heuristic function in ant colony system in order to reflect the new information discovered by the ants. Experimental results showed that enhanced algorithm provides better results than classical ant colony system in term of best, average and standard of the best tour length. Keywords : Ant Colony Optimization, Ant Colony System, Heuristic Function, Traveling Salesman Problem 1. Introduction Biological ants have the ability to discover the shortest route from the nest to the source of food [1]. Although they do not have an advanced vision system [2], they have the ability to communicate with the environment. Ants use a chemical substance called a “pheromone” to communicate with the environment and between each other [3]. Pheromone substance has an evaporation property which is a powerful mechanism to update the route information. While an ant moves looking for food, it deposits a pheromone along the path. The following ant will, more likely, select the route with richer pheromones. This mechanism will make the ant choose the shortest path. In 1992, Marco Dorigo proposed the first Ant Colony Optimization (ACO) algorithm to search for an optimal solution in graphs to solve optimization problems such as the travelling salesman problem, job scheduling and network routing [1]. The variants of ACO are: (i) Ant System (AS) [4] [5] [6]. (ii) The first improvement on the ant system, called the Elitist strategy for Ant System (EAS) [7]. The improvement was done by providing strong additional reinforcement to the arcs belonging to the best tour found since the start of the algorithm. (iii) Rank-Based Ant System (AS

Ant Colony Optimization. A Computational Intelligence Technique

Ant colony optimization (ACO) is a novel computational technique inspired by a foraging behavior of ants has been successfully applied for solving real world optimization problems. This behavioral pattern inspires artificial ants for the search of solutions to the various types of optimization problems. ACO is a probabilistic search approach founded on the idea of evolutionary process. In this paper, we present an overview of ant colony optimization and ACO variants up to now. we also summarize various types of applications. Finally we focus on some research efforts directed at receiving a dipper understanding of the ant colony optimization algorithms.

New Variants of Ant Colony Optimization for Network Routing

This paper suggests new variants of Ant Colony Optimization(ACO)Techniques for Network Routing. There are three existing variants of ACO based on pheromone deposit calculation. In our earlier work we suggested three different heuristics for selecting the next node at each step of iteration. Incorporation of these heuristics in each of the above three variants result into nine variations. In this paper the performance of these nine variations has been studied. Moreover, we have modified the pheromone deposit calculation considering the transmission time of each successful packet(ant) and incorporated this new pheromone update formula in each of the nine variants. As a result, we have obtained nine new variants of ACO. The performance of these new nine variants has been compared with previous ones with respect to the speed of execution, throughput and the number of successful packets. The experiments have been performed over two different network topologies. In one of the variant a tabu list has been incorporated. The length of the tabu list plays a vital role in improving the performance of the routing algorithm. In this paper it has been observed that the new variations of ACO have outperformed the previous ones. These new variants can perform efficient network routing in an environment having variable transmission time along the paths due to congestion or poor link quality.

Improved understanding of the searching behavior of ant colony optimization algorithms applied to the water distribution design problem

Evolutionary algorithms (EAs) have been applied successfully to many water resource problems, such as system design, management decision formulation, and model calibration. The performance of an EA with respect to a particular problem type is dependent on how effectively its internal operators balance the exploitation/exploration trade‐off to iteratively find solutions of an increasing quality. For a given problem, different algorithms are observed to produce a variety of different final performances, but there have been surprisingly few investigations into characterizing how the different internal mechanisms alter the algorithm's searching behavior, in both the objective and decision space, to arrive at this final performance. This paper presents metrics for analyzing the searching behavior of ant colony optimization algorithms, a particular type of EA, for the optimal water distribution system design problem, which is a classical NP‐hard problem in civil engineering. Using the proposed metrics, behavior is characterized in terms of three different attributes: (1) the effectiveness of the search in improving its solution quality and entering into optimal or near‐optimal regions of the search space, (2) the extent to which the algorithm explores as it converges to solutions, and (3) the searching behavior with respect to the feasible and infeasible regions. A range of case studies is considered, where a number of ant colony optimization variants are applied to a selection of water distribution system optimization problems. The results demonstrate the utility of the proposed metrics to give greater insight into how the internal operators affect each algorithm's searching behavior.

A Survey of Ant Colony Optimization Algorithms for Telecommunication Networks

Optical and ad-hoc networks which fulfill the communications requirements of complex applications must meet the Quality of Service (QoS) demanded by these applications, such as transmission delay. These demands are hard to satisfy in the presence of unpredictable behavior in the environment such as interference, traffic congestion, etc. Algorithms based on Ant Colony Optimization (ACO) offer an effective approach to meet such challenges since they are well suited to the dynamic routing optimization and dynamic resource reassignment required by these applications. In this paper, the author presents a survey of Ant Colony Optimization variants applied to ad-hoc and optical networks. The ACO variant called AntHocNet in particular will be reviewed, analyzed, and criticized from the point of view of emergent applications for environment management such as Intelligent Transportation Systems (ITS).

Applying Ant System for solving Unequal Area Facility Layout Problems

Ant Colony Optimization (ACO) is a young metaheuristic algorithm which has shown promising results in solving many optimization problems. To date, a formal ACO-based metaheuristic has not been applied for solving Unequal Area Facility Layout Problems (UA-FLPs). This paper proposes an Ant System (AS) (one of the ACO variants) to solve them. As a discrete optimization algorithm, the proposed algorithm uses slicing tree representation to easily represent the problems without too restricting the solution space. It uses several types of local search to improve its search performance. It is then tested using several case problems with different size and setting. Overall, the proposed algorithm shows encouraging results in solving UA-FLPs.

A hybrid ant colony optimization algorithm for solving facility layout problems formulated as quadratic assignment problems

PurposeThis paper aims to describe a new hybrid ant colony optimization (ACO) algorithm developed to solve facility layout problems (FLPs) formulated as quadratic assignment problems (QAPs).Design/methodology/approachA hybrid ACO algorithm which combines max‐min ant system (MMAS) (i.e. a variant of ACO) with genetic algorithm (GA) has been developed. The hybrid algorithm is further improved with the use of a novel minimum pheromone threshold strategy (MPTS).FindingsThe hybrid algorithm shows satisfactory results in the experimental evaluation due to the synergy and collaboration between MMAS and GA. The results also show that the use of MPTS helps them to achieve such performance, by promoting search diversification.Research limitations/implicationsThe experimental evaluation presented emphasizes more on the search performance or pattern of the hybrid algorithm. Detailed computational work could reveal other strengths of the algorithm.Practical implicationsThe developmental work presented in this paper could be used by researchers and practitioners to solve QAPs. Its use may also be expanded to solve other combinatorial optimization and engineering problems.Originality/valueThis paper provides useful insights into the development of a hybrid ACO algorithm that combines MMAS with GA for solving QAPs.