Adaptive Tabu Search Research Articles

The Enhanced – DQF Algorithm and Optimal Controller Design for Shunt Active Power Filter

This paper proposes the performance enhancement of harmonic detection using a dq–frame with Fourier algorithm (e–DQF) for a shunt active power filter (SAPF) in ideal voltage conditions. The reference current calculation for all harmonic orders and some harmonic orders mitigation using the e–DQF algorithm is described. Moreover, the optimal PI controller design of the compensating current control is also presented. The artificial intelligence (AI) technique called adaptive tabu search (ATS) is applied to design this controller. The performance comparison of the PI controller designed by both conventional and AI approaches are fully presented. The hardware in the loop (HIL) simulation based on eZdspTM F28335 board is used to simulate the harmonic elimination with SAPF in the system. The results show that the controller designed by the AI approach can provide the minimum total harmonic distortion of source currents (%THDi,av) after compensation compared with the conventional controller design.

Improving method of robust transmission network expansion planning considering intermittent renewable energy generation and loads

This study presents a novel method for robust transmission network expansion planning (RTNEP) considering intermittent renewable energy generation and loads. The investment cost of transmission line and operating cost are considered as the objective function of the planning. This study proposes to select the suitable scenarios to make the robust expansion plan for all possible scenarios based on intermittent renewable energy generation and loads data in a year. Meta-heuristic algorithm called adaptive tabu search (ATS) is employed in the proposed RTNEP. ATS iterates between the main problem, which minimises the investment cost and operation cost and the subproblem, which minimises the total power generation of conventional generators and curtailments of renewable energy generation and loads. This study uses AC load flow based on Newton–Raphson method and non-linear programming (NLP) based on interior point method for solving the main problem and the subproblem, respectively. IEEE Reliability Test System 79 (RTS 79) is used for testing the proposed method. The results show that the proposed expansion plan is more robust than the solutions from other research works.

An approach for evaluating the impact of an intermittent renewable energy source on transmission expansion planning

We propose a new robust optimization approach to evaluate the impact of an intermittent renewable energy source on transmission expansion planning (TEP). The objective function of TEP is composed of the investment cost of the transmission line and the operating cost of conventional generators. A method to select suitable scenarios representing the intermittent renewable energy generation and loads is proposed to obtain robust expansion planning for all possible scenarios. A meta-heuristic algorithm called adaptive tabu search (ATS) is employed in the proposed TEP. ATS iterates between the main problem, which minimizes the investment and operating costs, and the subproblem, which minimizes the cost of power generation from conventional generators and curtailments of renewable energy generation and loads. The subproblem is solved by nonlinear programming (NLP) based on an interior point method. Moreover, the impact of an intermittent renewable energy source on TEP was evaluated by comparing expansion planning with and without consideration of a renewable energy source. The IEEE Reliability Test System 79 (RTS 79) was used for testing the proposed method and evaluating the impact of an intermittent renewable energy source on TEP. The results show that the proposed robust optimization approach provides a more robust solution than other methods and that the impact of an intermittent renewable energy source on TEP should be considered.

Multiobjective Multipath Adaptive Tabu Search for Optimal PID Controller Design

The multipath adaptive tabu search (MATS) has been proposed as one of the most powerful metaheuristic optimization search techniques for solving the combinatorial and continuous optimization problems. The MATS employing the adaptive tabu search (ATS) as the search core has been proved and applied to various real-world engineering problems in single objective optimization manner. However, many design problems in engineering are typically multiobjective under complex nonlinear constraints. In this paper, the multiobjective multipath adaptive tabu search (mMATS) is proposed. The mMATS is validated against a set of multiobjective test functions, and then applied to design an optimal PID controller of the automatic voltage regulator (AVR) system. As results, the mMATS can provide very satisfactory solutions for all test functions as well as the control application.

Using the global optimisation methods to minimise the machining path length of the free-form surfaces in three-axis milling

During the machining of free-form surfaces using three-axis numerically controlled machine (NC), several parameters are chosen arbitrary and one of the most important is the feed motion direction. The main objective of this study is to minimise the machining time of complex surfaces while respecting a scallop height criteria. The analytical expression of the machining time is not known and by hypothesis, it is assumed to be proportional to the path length crossed by the cutting tool. This path length depends on the feed direction. To have an optimal feed direction at any point, the surface is divided into zones with low variation of the steepest slope direction. The optimization problem was formulated aiming at minimizing the global path length. Furthermore, a penalty reflecting the time loss due to the movement of the tool from one zone to another one is taken into account. Several heuristics are used to resolve this problem: Clarke and Wrights, Greedy randomized adaptive search procedure, Tabu search and Nearest neighbour search. An example illustrates our work by applying the different heuristics on a test surface. After simulations, the results obtained present a significant saving of paths length of 24% compared to the machining in one zone.

Parameter Identification of BLDC Motor Model Via Metaheuristic Optimization Techniques

The brushless dc (BLDC) motor has been increasingly used in industrial automation, automotive, aerospace, instrumentation and appliances. Analysis and design of the BLDC motor efficiently require its accurate model and parameters. In this paper, the parameter identification of the BLDC motor model via well-known metaheuristic optimization search techniques is proposed. Two trajectory-based methods, i.e. adaptive tabu search (ATS) and intensified current search (ICS) are employed to estimate the BLDC motor parameters. As simulation results of model identification and validation, both ATS and ICS can provide optimal BLDC model parameters. The BLDC models obtained show a very good agreement to actual system dynamics. However, the ICS can pro-vide optimal model parameters faster than the ATS.

An Artificial Intelligence Based System Identification of AC-DC Power System Including a Three-Phase Controlled Rectifier

The artificial intelligence techniques are widely applied to many works of engineering. This paper presents the application of the artificial intelligence techniques to identify the AC-DC power system parameters. The DC-link filters are normally included in such power systems. For some applications, accurate DC-link filter parameters are very important for the system analysis and design. According to the measurement errors and a large variation in a capacitor value, the parameter identification is needed to provide the accurate parameters. Therefore, the paper presents the identification of DC-link filter parameters using the adaptive tabu search technique and the genetic algorithm. The experimental results from the testing rig and the averaging model of the system are used in the searching process. The identification results show that the accurate DC-link filter parameters including line parameters on the AC side can be easily obtained via the proposed method. Moreover, the resulting parameters are very useful for a stability analysis of the power electronic systems due to a constant power load.

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.

Transmission network expansion planning considering renewable energy target with Taguchi's orthogonal array testing

Nowadays, renewable energy resources are becoming more and more popular. The intermittent nature of renewable energy sources will affect transmission network expansion planning (TNEP). Consequently, this paper presents a new problem formulation and solution procedure for TNEP which can cope with the intermittent characteristics. Taguchi's orthogonal array testing (TOAT) is used to select the scenarios that represent the loads and renewable energy resource uncertainties. In this paper, a new index called the ‘renewable energy leak’ (REL) is proposed to evaluate the ‘unutilized or leaked’ renewable energy in the TNEP. For minimizing the investment cost of transmission network expansion, annual generator operation cost, and annual transmission line loss cost, the meta‐heuristic adaptive Tabu search (ATS) algorithm is used, while the system operating limits including the REL index are taken into account as the optimization constraints. The proposed procedure is tested in a modified IEEE Reliability Test System 79. The results show that the proposed method is efficient and promising. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Application of Adaptive Tabu Search Algorithm in 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 (ATS) algorithm in the conventional Sinusoidal Fryze voltage (SFV) control technique. Optimization of the conventional Sinusoidal Fryze voltage control technique has been done using adaptive tabu search algorithm. This paper discusses about the comparative performances of conventional Sinusoidal Fryze voltage control strategy and ATS-optimized controllers. ATS algorithm has been used to obtain the optimum value of Kp and Ki. Analysis of the hybrid series active power filter system under non linear load condition and its impact on the performance of the controllers is evaluated. Effectiveness of the hybrid series active power filter to provide harmonic damping is demonstrated by MATLAB/Simulink results. Total harmonic distortion (THD) demonstrates the practical viability of the controller for hybrid series active power filter to provide harmonic isolation of non-linear loads and to comply with IEEE 519 recommended harmonic standards.

Instantaneous Power Theory with Fourier and Optimal Predictive Controller Design for Shunt Active Power Filter

This paper presents a novel harmonic identification algorithm of shunt active power filter for balanced and unbalanced three-phase systems based on the instantaneous power theory called instantaneous power theory with Fourier. Moreover, the optimal design of predictive current controller using an artificial intelligence technique called adaptive Tabu search is also proposed in the paper. These enhancements of the identification and current control parts are the aim of the good performance for shunt active power filter. The good results for harmonic mitigation using the proposed ideas in the paper are confirmed by the intensive simulation using SPS in SIMULINK. The simulation results show that the enhanced shunt active power filter can provide the minimum %THD (Total Harmonic Distortion) of source currents and unity power factor after compensation. In addition, the %THD also follows the IEEE Std.519-1992.

An Adaptive Tabu Search Method for Optimizing Procurement Plans under Correlated Demands

In this paper we develop an adaptive Tabu search method to facilitate retailers in deciding procurement plans while facing correlated demands. The Tabu search method examines mul-tiple candidate moves in a single iteration, and randomly accepts one of the candidate moves based the solution quality and an adaptive probability measure. We vary the acceptance probability so that Tabu search is able to switch between intensification and diversification. The Tabu search method is compared against a Hillclimbing-based construction method, and computational results demonstrate that Tabu search performed significantly better than the construction heuristics.

Applying an adaptive tabu search algorithm to optimize truck-dock assignment in the crossdock management system

Different from warehouse, crossdock is considered as a “JIT” technique in logistics and supply chain management (LSCM), which is usually a short period of time to store cargos. Since cargos transported to a crossdock by inbound trucks are immediately sorted out, repackaged, routed and loaded into outbound trucks and then delivered to customers within one day, one of the key issues to operate crossdock successfully is to develop an efficient truck-dock assignment module in the crossdock management system so that all the cargos can be delivered to customers on time. In this paper, we propose an adaptive tabu search (ATS) algorithm as an artificial intelligence (AI) tool to optimize the truck-dock assignment problem within a crossdock in a very efficient way, and computational experiments are conducted, showing that our approach dominates the CPLEX Solver in both effectiveness and efficiency.

Two approaches for solving the buffer allocation problem in unreliable production lines

Abstract This paper presents an integrated approach to solve the buffer allocation problem in unreliable production lines so as to maximize the throughput rate of the line with minimum total buffer size. The proposed integrated approach has two control loops; the inner loop and the outer loop. While the inner loop control includes an adaptive tabu search algorithm proposed by Demir et al. [8] , binary search and tabu search are proposed for the outer loop. These nested loops aim at minimizing the total buffer size to achieve the desired throughput level. To improve the efficiency of the proposed tabu search, alternative neighborhood generation mechanisms are developed. The performances of the proposed algorithms are evaluated by extensive computational experimentation, and the results are reported.

Current Search: Performance Evaluation and Application to DC Motor Speed Control System Design

This paper proposes the current search (CS) metaheuristics conceptualized from the electric current flowing through electric networks for optimization problems with continuous design variables. The CS algorithm possesses two powerful strategies, exploration and exploitation, for searching the global optimum. Based on the stochastic process, the derivatives of the objective function is unnecessary for the proposed CS. To evaluate its performance, the CS is tested against several unconstrained optimization problems. The results obtained are compared to those obtained by the popular search techniques, i.e., the genetic algorithm (GA), the particle swarm optimization (PSO), and the adaptive tabu search (ATS). As results, the CS outperforms other algorithms and provides superior results. The CS is also applied to a constrained design of the optimum PID controller for the dc motor speed control system. From experimental results, the CS has been successfully applied to the speed control of the dc motor.

A Comparison of Three Search Algorithms for Solving the Buffer Allocation Problem in Reliable Production Lines

This paper investigates the performance of three search algorithms: Myopic Algorithm, Adaptive Tabu Search and Degraded Ceiling to solve the buffer allocation problem in reliable production lines. DECO algorithm is used to calculate throughput. This algorithm is a variant of a decomposition algorithm specifically developed to solve large reliable production lines with parallel machines at each workstation and exponentially distributed service times. The measures of performance used are the CPU time required and closeness to the maximum throughput achieved. The three search algorithms are ranked in respect to these two measures and certain findings regarding their performances over the experimental set are given.

A Hybrid Algorithm for Open Vehicle Routing Optimization of Coal Mines Material

This study primarily focuses on solving a practical OVRP by applying a novel hybrid adaptive Genetic Algorithm and Tabu Search (GA-TS). The GA-TS combines the GA's parallel computing and global optimization with TS's Tabu search skill and fast local search. In the GA-TS Algorithm, individuals of population do TS local search with a certain degree of probability using natural number coding. It not only ensures global optimization, but also improves the speed of computing. The Algorithm is applied to optimize the mines material transport vehicle routing of China Zhengzhou Coal Mine and power Supply Co., Ltd. Through the applications, the optimal economical transport routing has been set up and economic costs have been greatly reduced.

Multipath adaptive tabu search: Its convergence and application to identification problem

This article presents new metaheuristics denoted as multipath adaptive tabu search (MATS). The algorithms employ adaptive tabu search (ATS) as the main search heuristics, and incorporate partitioning, sequencing, and discarding mechanisms, respectively. The algorithms are explained in details with their convergence analyses presented. The article also gives an illustrative application of identification of nonlinear force in a cart-plus-pendulum system. Key words: Metaheuristics, multipath adaptive tabu search, convergence, identification.