Li-Pei Wong

BEE COLONY OPTIMIZATION WITH LOCAL SEARCH FOR TRAVELING SALESMAN PROBLEM

Many real world industrial applications involve finding a Hamiltonian path with minimum cost. Some instances that belong to this category are transportation routing problem, scan chain optimization and drilling problem in integrated circuit testing and production. This paper presents a bee colony optimization (BCO) algorithm for traveling salesman problem (TSP). The BCO model is constructed algorithmically based on the collective intelligence shown in bee foraging behaviour. The model is integrated with 2-opt heuristic to further improve prior solutions generated by the BCO model. Experimental results comparing the proposed BCO model with existing approaches on a set of benchmark problems are presented.

A modified Intelligent Water Drops algorithm and its application to optimization problems

The Intelligent Water Drop (IWD) algorithm is a recent stochastic swarm-based method that is useful for solving combinatorial and function optimization problems. In this paper, we investigate the effectiveness of the selection method in the solution construction phase of the IWD algorithm. Instead of the fitness proportionate selection method in the original IWD algorithm, two ranking-based selection methods, namely linear ranking and exponential ranking, are proposed. Both ranking-based selection methods aim to solve the identified limitations of the fitness proportionate selection method as well as to enable the IWD algorithm to escape from local optima and ensure its search diversity. To evaluate the usefulness of the proposed ranking-based selection methods, a series of experiments pertaining to three combinatorial optimization problems, i.e., rough set feature subset selection, multiple knapsack and travelling salesman problems, is conducted. The results demonstrate that the exponential ranking selection method is able to preserve the search diversity, therefore improving the performance of the IWD algorithm.

An ensemble of intelligent water drop algorithms and its application to optimization problems

The Intelligent Water Drop (IWD) algorithm is a recent stochastic swarm-based method that is useful for solving combinatorial and function optimization problems. In this paper, we propose an IWD ensemble known as the Master-River, Multiple-Creek IWD (MRMC-IWD) model, which serves as an extension of the modified IWD algorithm. The MRMC-IWD model aims to improve the exploration capability of the modified IWD algorithm. It comprises a master river which cooperates with multiple independent creeks to undertake optimization problems based on the divide-and-conquer strategy. A technique to decompose the original problem into a number of sub-problems is first devised. Each sub-problem is then assigned to a creek, while the overall solution is handled by the master river. To empower the exploitation capability, a hybrid MRMC-IWD model is introduced. It integrates the iterative improvement local search method with the MRMC-IWD model to allow a local search to be conducted, therefore enhancing the quality of solutions provided by the master river. To evaluate the effectiveness of the proposed models, a series of experiments pertaining to two combinatorial problems, i.e., the travelling salesman problem (TSP) and rough set feature subset selection (RSFS), are conducted. The results indicate that the MRMC-IWD model can satisfactorily solve optimization problems using the divide-and-conquer strategy. By incorporating a local search method, the resulting hybrid MRMC-IWD model not only is able to balance exploration and exploitation, but also to enable convergence towards the optimal solutions, by employing a local search method. In all seven selected TSPLIB problems, the hybrid MRMC-IWD model achieves good results, with an average deviation of 0.021% from the best known optimal tour lengths. Compared with other state-of-the-art methods, the hybrid MRMC-IWD model produces the best results (i.e. the shortest and uniform reducts of 20 runs) for all13 selected RSFS problems.

Solving Asymmetric Traveling Salesman Problems using a generic Bee Colony Optimization framework with insertion local search

The Asymmetric Traveling Salesman Problem (ATSP) is one of the Combinatorial Optimization Problems that has been intensively studied in computer science and operations research. Solving ATSP is NP-hard and it is harder if the problem is with large scale data. This paper intends to address the ATSP using an hybrid approach which integrates the generic Bee Colony Optimization (BCO) framework and an insertion-based local search procedure. The generic BCO framework computationally realizes the bee foraging behaviour in a typical bee colony where bees travel across different locations to discover new food sources and perform waggle dances to recruit more bees towards newly discovered food sources. Besides the bee foraging behaviour, the generic BCO framework is enriched with an initialization engine, a fragmented solution construction mechanism, a local search and a pruning strategy. When the proposed algorithm is tested on a set of 27 ATSP benchmark problem instances, 37% of the benchmark instances are constantly solved to optimum. 89% of the problem instances are optimally solved for at least once. On average, the proposed BCO algorithm is able to obtain 0.140% deviation from known optimum for all the 27 instances. In terms of the average computational time, the proposed algorithm requires 48.955s (<; 1 minutes) to obtain the best tour length for each instance.

An artificial bee colony algorithm with a modified choice function for the Traveling Salesman Problem

The Artificial Bee Colony (ABC) algorithm is a swarm intelligence approach which has initially been proposed to solve optimization of mathematical test functions with a unique neighbourhood search mechanism. However, this neighbourhood search mechanism could not be directly applied to combinatorial discrete optimization problems. The employed and onlooker bees need to be equipped with problem-specific perturbative heuristics in order to tackle combinatorial discrete optimization problems. However, there is a large variety of available problem-specific heuristics. In this paper, a hyper-heuristic method, namely a Modified Choice Function (MCF), is applied such that it can regulate the selection of the neighbourhood search heuristics adopted by the employed and onlooker bees automatically. The proposed MCF-based ABC model is implemented using the Hyper-heuristic Flexible Framework (HyFlex). To demonstrate the effectiveness of the proposed model, ten Traveling Salesman Problem (TSP) instances available in HyFlex have been evaluated. The empirical results show that the proposed model is able to statistically outperform four out of five ABC variants throughout the optimization process.

Automatic design of hyper-heuristic based on reinforcement learning

Abstract Hyper-heuristic is a class of methodologies which automates the process of selecting or generating a set of heuristics to solve various optimization problems. A traditional hyper-heuristic model achieves this through a high-level heuristic that consists of two key components, namely a heuristic selection method and a move acceptance method. The effectiveness of the high-level heuristic is highly problem dependent due to the landscape properties of different problems. Most of the current hyper-heuristic models formulate a high-level heuristic by matching different combinations of components manually. This article proposes a method to automatically design the high-level heuristic of a hyper-heuristic model by utilizing a reinforcement learning technique. More specifically, Q-learning is applied to guide the hyper-heuristic model in selecting the proper components during different stages of the optimization process. The proposed method is evaluated comprehensively using benchmark instances from six problem domains in the Hyper-heuristic Flexible Framework. The experimental results show that the proposed method is comparable with most of the top-performing hyper-heuristic models in the current literature.

An ensemble of intelligent water drop algorithm for feature selection optimization problem

Abstract Master River Multiple Creeks Intelligent Water Drops (MRMC-IWD) is an ensemble model of the intelligent water drop, whereby a divide-and-conquer strategy is utilized to improve the search process. In this paper, the potential of the MRMC-IWD using real-world optimization problems related to feature selection and classification tasks is assessed. An experimental study on a number of publicly available benchmark data sets and two real-world problems, namely human motion detection and motor fault detection, are conducted. Comparative studies pertaining to the features reduction and classification accuracies using different evaluation techniques (consistency-based, CFS, and FRFS) and classifiers (i.e., C4.5, VQNN, and SVM) are conducted. The results ascertain the effectiveness of the MRMC-IWD in improving the performance of the original IWD algorithm as well as undertaking real-world optimization problems.

A Bee Colony Optimization algorithm with Frequent-closed-pattern-based Pruning Strategy for Traveling Salesman Problem

Bees perform waggle dance in order to communicate the information of food source to their hive mates. This unique foraging behaviour has been computationally realized as an algorithmic tool named the Bee Colony Optimization (BCO) algorithm to solve different types of Combinatorial Optimization Problems such as Traveling Salesman Problem (TSP). In order to enhance the performance of the BCO algorithm, it is integrated with a local optimization approach and a pruning strategy named as the Frequency-based Pruning Strategy (FBPS), which allows a subset of bees to undergo the local optimization and hence reduces the high processing overhead of the local optimization. Although the local optimization and the FBPS enhance the performance of the BCO algorithm, the FBPS becomes not scalable when building blocks in various sizes are considered in its pruning operation. This paper proposes a pruning strategy which employs the bi-directional extension (BIDE) based frequent closed pattern mining algorithm. It is named as the Frequent-closed-pattern-based Pruning Strategy (FCPBPS). The FCPBPS consists of two major operations: solutions accumulation and pruning operation. Solutions generated by bees are accumulated throughout the BCO algorithm execution. Based on the accumulated solutions, a set of frequent closed patterns in various sizes is mined using the BIDE algorithm. This set of frequent closed patterns is used in the FCPBPS pruning operation such that only relatively better bees (i.e. bees that produce solution which contains many of these frequent closed patterns) are allowed to undergo the local optimization. A total of 18 selected symmetric TSP benchmark problems range from 318 cities to 1291 cities are used as the testbed of this research. On average, the experimental results show that the FCPBPS requires 20.2% lesser computational time compared to the FBPS, to yield similar best-so-far TSP tour lengths.

An artificial bee colony algorithm with a Modified Choice Function for the traveling salesman problem

Abstract The Artificial Bee Colony (ABC) algorithm is a swarm intelligence approach which has initially been proposed to solve optimisation of mathematical test functions with a unique neighbourhood search mechanism. This neighbourhood search mechanism could not be directly applied to combinatorial discrete optimisation problems. In order to tackle combinatorial discrete optimisation problems, the employed and onlooker bees need to be equipped with problem-specific perturbative heuristics. However, a large variety of problem-specific heuristics are available, and it is not an easy task to select an appropriate heuristic for a specific problem. In this paper, a hyper-heuristic method, namely a Modified Choice Function (MCF), is applied such that it can regulate the selection of the neighbourhood search heuristics adopted by the employed and onlooker bees automatically. The Lin-Kernighan (LK) local search strategy is integrated to improve the performance of the proposed model. To demonstrate the effectiveness of the proposed model, 64 Traveling Salesman Problem (TSP) instances available in TSPLIB are evaluated. On average, the proposed model solves the 64 instances to 0.055% from the known optimum within approximately 2.7 min. A performance comparison with other state-of-the-art algorithms further indicates the effectiveness of the proposed model.

A grapheme and phone rescoring combination system for Malay broadcast news recognition

The main motivation of this paper is to improve the automatic speech recognition (ASR) hypothesis in the Malay language. Manual news transcription is too expensive and takes a long time. Hence, without an ASR system, access to audio archives and searches within them would be restricted to the limited number of textual documents that have been manually transcribed by humans or indexed with keywords. Multiple hypotheses are useful because the single best recognition output still has numerous errors, even for state-of-the-art systems. In this paper, we propose an approach to reduce the word error rate (WER) in an ASR hypothesis. This approach is known as the three-pass combination method using parallel ASR systems. The three-pass combination system based on grapheme rescoring and phone rescoring re-evaluates all of the hypotheses produced by the ASR systems to produce a more accurate hypothesis. To evaluate the performance of the proposed approach, Malay broadcast news contains speech from newscaster, reporter and interviewers in noisy environments recorded from Malaysia local news channels are employed. This approach reduced the WER by 4.4% from 34.5% to 30.1%. The performance of the proposed approach was compared with six approaches that are frequently used for ASR rescoring and combination.