The behaviour of ACS-TSP algorithm when adapting both pheromone parameters using fuzzy logic controller

In this paper, an evolved ant colony system (ACS) is proposed by dynamically adapting the responsible parameters for the decay of the pheromone trails and using fuzzy logic controller (FLC) applied in the travelling salesman problems (TSP). The purpose of the proposed method is to understand the effect of both parameters and on the performance of the ACS at the level of solution quality and convergence speed towards the best solutions through studying the behavior of the ACS algorithm during this adaptation. The adaptive ACS is compared with the standard one. Computational results show that the adaptive ACS with dynamic adaptation of local pheromone parameter is more effective compared to the standard ACS

A statistical learning based approach for parameter fine-tuning of metaheuristics

Metaheuristics are approximation methods used to solve combinatorial optimization problems. Their performance usually depends on a set of parameters that need to be adjusted. The selection of appropriate parameter values causes a loss of efficiency, as it requires time, and advanced analytical and problem-specific skills. This paper provides an overview of the principal approaches to tackle the Parameter Setting Problem, focusing on the statistical procedures employed so far by the scientific community. In addition, a novel methodology is proposed, which is tested using an already existing algorithm for solving the Multi-Depot Vehicle Routing Problem.Peer ReviewedPostprint (published version

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 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

Tuning Parameter pada Pengendali Logika Fuzzy menggunakan Algoritma Ant Colony Optimization

The Ant Colony Optimization (ACO) algorithm can be applied in tuning parameters in a Fuzzy Logic Controller (FLC) to control the water level of the process tank. Fuzzy input and output consists of seven membership functions, namely large positive (PB), medium positive (PM) and small positive (PS), zero (Z), small negative (NS), medium negative (NM) and large negative (NB) ). First, the initial FLC parameter is searched, then a graph is generated where the values ​​of the FLC parameter are determined in the range of values ​​between 0 and 1.5 times the initial parameter value. ACO algorithm is used to improve the value of the FLC parameter in order to obtain better performance. The expected controller performance is to minimize the maximum surge (overshoot) and rise time. This system is implemented using the LabVIEW program. Water level data is obtained using a potentiometer sensor. The output from the FLC is connected to the stepper motor to regulate the discharge of water input to the process tank. The test results obtained overshoot and a small rise time, for example, for setpoint 8, the system output performance has an overshoot of 2.5% and a rise time of 8909 ms. ACO algorithm succeeded in increasing system performance compared to system performance if using initial parameters. This increase in performance is due to the ACO algorithm acting as a local search algorithm which will look for better system performance around its initial parameter values. This research successfully demonstrated that the ACO algorithm can be used to do tuning from FLC parameters.Algoritma Ant Colony Optimization (ACO) dapat diterapkan dalam tuning parameter pada pengendali logika fuzzy atau Fuzzy Logic Controller (FLC) untuk mengendalikan ketinggian air dari tangki proses. Masukan dan keluaran fuzzy terdiri dari tujuh fungsi keanggotaan, yaitu positif besar (PB), positif menengah (PM) dan positif kecil (PS), zero (Z), negatif kecil (NS), negatif menengah (NM) dan negatif besar (NB). Pertama-tama, dicari parameter FLC awal, lalu dibangkitkan suatu graph dimana nilai-nilai parameter FLC ditentukan dalam rentang nilai antara 0 hingga 1,5 kali dari nilai parameter awal. Algoritma ACO digunakan untuk memperbaiki nilai parameter FLC tersebut agar diperoleh performansi yang lebih baik. Performansi pengendali yang diharapkan adalah meminimalkan lonjakan maksimum (overshoot) dan waktu naik (rise time). Sistem ini diimplementasikan menggunakan program labVIEW. Data ketinggian air diperoleh menggunakan sensor potensiometer. Keluaran dari FLC terhubung dengan  motor stepper untuk mengatur debit masukan air ke tangki proses. Hasil pengujian  diperoleh overshoot dan rise time yang kecil, sebagai contoh, untuk setpoint 8, performansi keluaran sistem memiliki  overshoot 2.5% dan rise time 8909 ms. Algoritma ACO berhasil meningkatkan performansi sistem dibandingkan performansi sistem jika menggunakan parameter awal. Peningkatan performansi ini dikarenakan algoritma ACO bertindak sebagai algoritma pencarian lokal (local search) yang akan mencari performansi sistem yang lebih baik disekitar nilai parameter awalnya. Penelitian ini berhasil menunjukkan bahwa algoritma ACO dapat digunakan untuk melakukan tuning dari parameter FLC

A statistical learning based approach for parameter fine-tuning of metaheuristics

Metaheuristics are approximation methods used to solve combinatorial optimization problems. Their performance usually depends on a set of parameters that need to be adjusted. The selectionof appropriate parameter values causes a loss of efficiency, as it requires time, and advanced analytical and problem-specific skills. This paper provides an overview of the principal approaches to tackle the Parameter Setting Problem, focusing on the statistical procedures employed so far by the scientific community. In addition, a novel methodology is proposed, which is tested using an already existing algorithm for solving the Multi-Depot Vehicle Routing Problem.Peer Reviewe

A statistical learning based approach for parameter fine-tuning of metaheuristics

Metaheuristics are approximation methods used to solve combinatorial optimization problems. Their performance usually depends on a set of parameters that need to be adjusted. The selectionof appropriate parameter values causes a loss of efficiency, as it requires time, and advanced analytical and problem-specific skills. This paper provides an overview of the principal approaches to tackle the Parameter Setting Problem, focusing on the statistical procedures employed so far by the scientific community. In addition, a novel methodology is proposed, which is tested using an already existing algorithm for solving the Multi-Depot Vehicle Routing Problem

Nature-inspired parameter controllers for ACO-based reactive search

This study proposes machine learning strategies to control the parameter adaptation in ant colony optimization algorithm, the prominent swarm intelligence metaheuristic.The sensitivity to parameters’ selection is one of the main limitations within the swarm intelligence algorithms when solving combinatorial problems.These parameters are often tuned manually by algorithm experts to a set that seems to work well for the problem under study, a standard set from the literature or using off-line parameter tuning procedures. In the present study, the parameter search process is integrated within the running of the ant colony optimization without incurring an undue computational overhead.The proposed strategies were based on a novel nature-inspired idea. The results for the travelling salesman and quadratic assignment problems revealed that the use of the augmented strategies generally performs well against other parameter adaptation methods