Adaptive Tabu Search Algorithm Research Articles

Study on Application of AI Controllers for Speed Control of Motors

Building high-performance motor drives is vital for industrial uses. A high-performance motor drive system must be quick in terms of dynamic speed order monitoring and load regulation. The motor controllers can provide system protection by regulating or limiting torque, protecting against overloads, and safeguarding against mistakes. Many motor controllers include logic for managing applications as well as additional features like data recording and data collecting. The purpose is to study various tuning techniques for motor speed controllers using Artificial intelligence. The controllers such as proportional-integral (PI) controller and proportional integral derivative (PID) controller have been considered here. Most frequently used Artificial intelligence (AI) methods such as Artificial Neural Networks (ANN), Fuzzy logic controller (FLC), Genetic Algorithm (GA), Bat Algorithm, Adaptive Tabu Search (ATS), Ant Colony Optimization (ACO), Ziegler and Nichols (ZN) Algorithm are considered by their decision-making capability. In the proposed work a rigorous literature review is done for analyzing the performance of AI-based controllers. This will help other researchers to understand various aspects of the said controllers, particularly the technology involved. In-depth review and application of AI controllers are highlighted using various charts and graphs for easy understanding.
 Keywords: AI motor controllers, Fuzzy Logic, PID controller, PI controller, Genetic algorithm, PSO algorithm, Ziegler and Nichols algorithm, Bat algorithm, Adaptive Tabu search algorithm

An Adaptive Tabu Search Algorithm for Solving the Two-Dimensional Loading Constrained Vehicle Routing Problem with Stochastic Customers

In practical logistic distributions, uncertainties may exist in each distribution process, and sometimes suppliers have to take undesirable measures to deal with the subsequent schedule variances. In light of the uncertainty of customers in logistics distribution and the widely applied two-dimensional loading patterns in transportation, we propose and formulate a two-dimensional loading-constrained vehicle routing problem with stochastic customers (2L-VRPSC), where each customer has a known probability of presence and customers’ demands are a set of non-stackable items. A stochastic modeling platform of 2L-VRPSC is established based on a Monte Carlo simulation and scenario analysis to minimize the expected total transportation cost. To achieve this, an enhanced adaptive tabu search (EATS) algorithm incorporating the multi-order bottom-fill-skyline (MOBFS) packing heuristic is proposed, where the EATS algorithm searches for the optimal routing combination and the MOBFS checks the feasibility of each route and guides the EATS to search for feasible solutions. The widely used two-dimensional loading-constrained vehicle routing problem (2L-VRP) benchmarks under different loading configurations considering items’ sequential and rotation constraints are applied for experiments, which demonstrates the comparable efficiency of the proposed EATS-MOBFS for solving 2L-VRP. Furthermore, the results and analysis of experiments based on the new 2L-VRPSC instances verify the versatility of the proposed solving approach, which is capable of providing more practical solutions to some real-life scenarios with customers’ uncertain information.

Optimal Speed Controller Design of Commercial BLDC Motor by Adaptive Tabu Search Algorithm

Optimal Speed Controller Design of Commercial BLDC Motor by Adaptive Tabu Search Algorithm

An Advanced and Adaptive Tabu Search Algorithm for Dynamic Shared Parking Reservation and Allocation

An Advanced and Adaptive Tabu Search Algorithm for Dynamic Shared Parking Reservation and Allocation

A Study of Distributed Earth Observation Satellites Mission Scheduling Method Based on Game-Negotiation Mechanism.

While monolithic giant earth observation satellites still have obvious advantages in regularity and accuracy, distributed satellite systems are providing increased flexibility, enhanced robustness, and improved responsiveness to structural and environmental changes. Due to increased system size and more complex applications, traditional centralized methods have difficulty in integrated management and rapid response needs of distributed systems. Aiming to efficient missions scheduling in distributed earth observation satellite systems, this paper addresses the problem through a networked game model based on a game-negotiation mechanism. In this model, each satellite is viewed as a “rational” player who continuously updates its own “action” through cooperation with neighbors until a Nash Equilibria is reached. To handle static and dynamic scheduling problems while cooperating with a distributed mission scheduling algorithm, we present an adaptive particle swarm optimization algorithm and adaptive tabu-search algorithm, respectively. Experimental results show that the proposed method can flexibly handle situations of different scales in static scheduling, and the performance of the algorithm will not decrease significantly as the problem scale increases; dynamic scheduling can be well accomplished with high observation payoff while maintaining the stability of the initial plan, which demonstrates the advantages of the proposed methods.

Energy Saving Approach for an Electric Pump Using a Fuzzy Controller

This paper presents an energy-saving approach for electric pumps widely used in agriculture. A capacitor-run single-phase induction motor is used with a centrifugal pump. An appropriate energy-saving frequency and voltage calculation algorithm is proposed in this paper. The fuzzy controller is used to control the water flow rate of the electric pump. Moreover, the adaptive Tabu search algorithm is used to identify induction motor parameters. The experimental results from the energy-saving approach are compared with the valve control and V/f control in terms of input power and power factor. From the experimental results, the electric pump using the proposed energy-saving approach consumes minimum input power compared with other approaches. In addition, the energy-saving approach can provide a good power factor at any flow rate.

An adaptive tabu search algorithm embedded with iterated local search and route elimination for the bike repositioning and recycling problem

The bike repositioning and recycling problem (BRRP) is significant to develop a sustainable bike-sharing system and can effectively reduce the imbalance between demand and supply. This study investigates the static repositioning and recycling problem of a bike-sharing system, which is formulated as an integer linear programming model. The BRRP is a variant of the multi-depot simultaneous pickup and delivery problem with multi-commodity demand. To solve the proposed model, an adaptive tabu search (ATS) algorithm combined with six neighborhood structures is developed. Moreover, an iterated local search (ILS) and a route elimination operator are both embedded to reduce the number of routes. The performance of the proposed ATS is evaluated by comparing it with tabu search (TS) and variable neighborhood search (VNS). The experimental results show that the proposed algorithm performs better than TS and VNS in terms of the solution quality. The proposed ATS was used to analyze the bicycle system in New York City. Finally, a free solver is developed for the bike repositioning and recycling problem, which is named the BRRP Solver.

Heterogeneous fixed fleet vehicle routing problem based on fuel and carbon emissions

In this paper, we study an emission-based heterogeneous fixed fleet vehicle routing problem (E-HFFVRP) with considerations of fuel and carbon emissions. This problem involves routing a fleet of a fixed number of vehicles with various capacities to serve a set of customers. It seeks to minimize the objective function, which incorporates the fixed expenses and variable costs consisting of fuel consumptions and carbon emissions. It is a new variant of the heterogeneous fixed fleet vehicle routing problem (HFFVRP), in which a fleet consists of a fixed number of vehicles with different capacities, fixed costs and variable costs. We formulate this problem with a mixed integer programming model by introducing an approach to calculate fuel and carbon emissions. Moreover, a split-based adaptive tabu search (SATS) algorithm using an optimal split scheme and an adaptive tabu search algorithm is proposed. Its key features and components are designed accordingly. Results of numerical experimentations on two sets of generated instances confirm the efficiency and effectiveness of the algorithm.

Tabu search algorithm for the distance-constrained vehicle routing problem with split deliveries by order.

The vehicle routing problem (VRP) has a wide range of applications in the field of logistics distribution. In order to reduce the cost of logistics distribution, the distance-constrained and capacitated VRP with split deliveries by order (DCVRPSDO) was studied. We show that the customer demand, which can’t be split in the classical VRP model, can only be discrete split deliveries by order. A model of double objective programming is constructed by taking the minimum number of vehicles used and minimum vehicle traveling cost as the first and the second objective, respectively. This approach contains a series of constraints, such as single depot, single vehicle type, distance-constrained and load capacity limit, split delivery by order, etc. DCVRPSDO is a new type of VRP. A new tabu search algorithm is designed to solve the problem and the examples testing show the efficiency of the proposed algorithm. This paper focuses on constructing a double objective mathematical programming model for DCVRPSDO and designing an adaptive tabu search algorithm (ATSA) with good performance to solving the problem. The performance of the ATSA is improved by adding some strategies into the search process, including: (a) a strategy of discrete split deliveries by order is used to split the customer demand; (b) a multi-neighborhood structure is designed to enhance the ability of global optimization; (c) two levels of evaluation objectives are set to select the current solution and the best solution; (d) a discriminating strategy of that the best solution must be feasible and the current solution can accept some infeasible solution, helps to balance the performance of the solution and the diversity of the neighborhood solution; (e) an adaptive penalty mechanism will help the candidate solution be closer to the neighborhood of feasible solution; (f) a strategy of tabu releasing is used to transfer the current solution into a new neighborhood of the better solution.

A New TS Algorithm for Solving Low-Carbon Logistics Vehicle Routing Problem with Split Deliveries by Backpack-From a Green Operation Perspective.

In order to promote the development of low-carbon logistics and economize logistics distribution costs, the vehicle routing problem with split deliveries by backpack is studied. With the help of the model of classical capacitated vehicle routing problem, in this study, a form of discrete split deliveries was designed in which the customer demand can be split only by backpack. A double-objective mathematical model and the corresponding adaptive tabu search (TS) algorithm were constructed for solving this problem. By embedding the adaptive penalty mechanism, and adopting the random neighborhood selection strategy and reinitialization principle, the global optimization ability of the new algorithm was enhanced. Comparisons with the results in the literature show the effectiveness of the proposed algorithm. The proposed method can save the costs of low-carbon logistics and reduce carbon emissions, which is conducive to the sustainable development of low-carbon logistics.

A tabu search algorithm for distribution network optimization with discrete split deliveries and soft time windows

To save the distribution network costs, the vehicle routing problem with split deliveries by order and soft time windows is studied. In this paper, the flexibility of the distribution system is enhanced via soft time windows, the requirement for non-split deliveries is loosened, and a new form of discrete split deliveries is defined. The new form of split deliveries by order is agreed for splitting. Giving considerations to split deliveries by order, soft time windows and working time constraints, a corresponding multi-objective mathematical model is constructed, and an adaptive tabu search algorithm with dynamic tabu list and multi-neighborhood structure is designed to solve the problem. The effectiveness of the new algorithm is verified by the example testing and comparison.

An Adaptive Tabu Search Algorithm for the Open Vehicle Routing Problem with Split Deliveries by Order

In order to reduce the cost of logistics distribution, a capacitated open vehicle routing problem with split deliveries by order is studied. According to the actual situation of split deliveries by order in the practice of logistics distribution, the condition of non-split in the traditional open vehicle routing problem is relaxed, and a new form of discrete split deliveries by order is designed. Based on the classical model of open vehicle routing problem, a corresponding double objective mathematical model with split deliveries by order is established, and an adaptive tabu search algorithm is designed to solve the problem. In order to improve the optimization performance, an adaptive penalty mechanism is designed, a multiple neighborhood structure is constructed, and the tabu list is reinitialized. The comparison with other methods in the literature shows the effectiveness of the algorithm.

The Optimal Controller Design of Buck-Boost Converter by using Adaptive Tabu Search Algorithm Based on State-Space Averaging Model

Normally, the artificial intelligence algorithms are widely applied to the optimal controller design. Then, it is expected that the best output performance is achieved. Unfortunately, when resulting controller parameters are implemented by using the practical devices, the output performance cannot be the best as expected. Therefore, the paper presents the optimal controller design using the combination between the state-space averaging model and the adaptive Tabu search algorithm with the new criteria as two penalty conditions to handle the mentioned problem. The buck-boost converter regulated by the cascade PI controllers is used as the example power system. The results show that the output performance is better than those from the conventional design method for both input and load variations. Moreover, it is confirmed that the reported controllers can be implemented using the realistic devices without the limitation and the stable operation is also guaranteed. The results are also validated by the simulation using the topology model of MATLAB and also experimentally verified by the testing rig.

Balanced Adaptive Tabu Search Algorithm to Optimize Dual-gantry Pick-and-place Assembly

This paper presents an optimization study of dual-gantry high-speed rotary-type pick-and-place surface mount device (SMD) machine. In the dual-gantry SMD machine, there are two feeder bases and gantry heads, such that two placement heads can pick-and-place synchronously in one PCB, which has an advantage in assembly PCB with more component types. The dual-gantry optimization problem is decomposed into nozzle arrangement, feeder assignment, pick-and-place sequence, component allocation, and gantry schedule problems. To balance the workload between gantries and reduce the wait time in the gantry's synchronous movement, a balanced adaptive tabu search (BATS) algorithm is proposed. Based on the 16 industrial data tests, the proposed algorithm yields a 7.04% improvement on average compared to the industrial package.

Optimized Aircraft Electric Control System Based on Adaptive Tabu Search Algorithm and Fuzzy Logic Control

Three conventional control constant instantaneous power control, sinusoidal current control, and synchronous reference frame techniques for extracting reference currents for shunt active power filters have been optimized using Fuzzy Logic control and Adaptive Tabu search Algorithm and their performances have been compared. Critical analysis of Comparison of the compensation ability of different control strategies based on THD and speed will be done, and suggestions will be given for the selection of technique to be used. The simulated results using MATLAB model are presented, and they will clearly prove the value of the proposed control method of aircraft shunt APF. The waveforms observed after the application of filter will be having the harmonics within the limits and the power quality will be improved.

Comparison of Soft Computing Techniques applied in High Frequency Aircraft System

Sinusoidal Current Control strategy for extracting reference currents for shunt active power filters have been optimized using Fuzzy Logic control and Adaptive Tabu search Algorithm and their performances have been compared. The acute analysis of Comparison of the compensation capability based on THD and speedwell be done, and recommendations will be given for the choice of technique to be used. The simulated results using MATLAB model are shown, and they will undoubtedly prove the importance of the proposed control technique of aircraft shunt APF.

The Energy Saving for Separately Excited DC Motor Drive via Model Based Method

The model based method for energy saving of the separately excited DC motor drive system is proposed in the paper. The accurate power loss model is necessary for this method. Therefore, the adaptive tabu search algorithm is applied to identify the parameters in the power loss model. The field current values for minimum power losses at any load torques and speeds are calculated by the proposed method. The rule based controller is used to control the field current and speed of the motor. The experimental results confirm that the model based method can successfully provide the energy saving for separately excited DC motor drive. The maximum value of the energy saving is 48.61% compared with the conventional drive method.

A novel fill-time window minimisation problem and adaptive parallel tabu search algorithm in mail-order pharmacy automation system

This paper presents a novel fill-time window (FTW) problem in a mail-order pharmacy automation (MOPA) system. The MOPA system uses a batch process to fulfil and distribute tens of thousands of highly customised prescription orders. It has been utilised to accommodate an increasing prescription volume and pharmacy dispensing productivity. Since the majority of prescription orders consist of multiple medications, the long medications’ waiting time in the collation process will increase the makespan or even cause a production deadlock in extreme cases. To minimise the collation time of multiple medication orders, the FTW is defined as the time difference between the first and last dispensed medications within a prescription order and the FTW problem is introduced as a flexible order scheduling problem by considering makespan as a constraint. To minimise the FTW, an integer mathematical model has been developed to find an optimal production schedule. To solve this NP-hard order scheduling problem efficiently, an adaptive parallel tabu search (APTS) algorithm is proposed. The performance of the proposed algorithm has been experimented with different system parameters. Based on the experimental results, the APTS algorithm yields less FTW than LPT, and less FTW than TS.

An optimal allocation of distributed generation and voltage control devices for voltage regulation considering renewable energy uncertainty

Distributed generation (DG) may result in voltage fluctuation by changing line flow and reactive power injection, especially DG that generates power from renewable energy resources. To cope with this problem, this paper proposes an optimization process to optimally regulate the system voltage profile to lie close to the desired values by using the adaptive Tabu search (ATS) algorithm. The system voltages will be regulated by using dispatchable DG and voltage control devices, i.e. voltage regulator and capacitor. Moreover, probabilistic load flow calculation by using Monte Carlo simulation is chosen to evaluate the uncertainty of DG powered by renewable energy resources. The number of switching operations of the voltage regulator and capacitor are also accounted for in the optimization constraints, as excessive frequent switching operations can damage these devices. The optimal sizes and locations of dispatchable DGs and capacitors are considered as the optimization variables. The proposed method is demonstrated in an IEEE 34‐bus distribution test system and a modified 21‐bus Provincial Electricity Authority (PEA) system (Thailand). © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Performance Evaluation of Adaptive Tabu Search Algorithm Optimized Sinusoidal Fryze Voltage Control based Hybrid Series Active Power Filter

A novel hybrid series active power filter to eliminate harmonics and compensate reactive power is presented and analyzed. The proposed active compensation technique is based in a hybrid series active filter using adaptive tabu search algorithm in the conventional Sinusoidal Fryze voltage control technique. The paper analyzes the proposed hybrid series active power filter when connected with load of 3-phase diode bridge rectifier with R-L branch directly. This paper intends to show the improvement between the performance of conventional scheme and ATS-optimized scheme. Simulation results obtained using MATLAB/Simulink confirm the viability of the proposed compensation technique.