Tabu Search Technique Research Articles

Optimizing architectural multi-dimensional forms; a hybrid approach integrating approximate evolutionary search, clustering and local optimization

Parametric tools in architecture allow for the design of complex and multi-dimensional forms on building facades. Among these, geometric patterns can mitigate direct sunlight and enhance energy efficiency. However, conventional simulation methods and available optimization tools are prohibitively expensive for optimizing such complex forms. To address this challenge, this study proposes two innovative hybrid workflows that integrate parametric modeling, evolutionary approximate and accurate models (NSGA-III), clustering through k-means algorithm, and local search (Tabu search) technique. The outcomes obtained from employing these hybrid approaches demonstrate a substantial reduction in computational time and costs while simultaneously achieving optimal results. Additionally, an extensive comparison between the two proposed methodologies is presented encompassing factors such as performance metrics and computational expenses incurred during implementation. The findings derived from pattern optimization reveal several key insights: increasing pattern counts; dispersing them across the facade; minimizing the distance between the pattern wall from windows; adopting a south-facing orientation with positive vertical rotation – all contribute towards diminishing energy consumption (measured by Energy Use Intensity or EUI) within cold climates. However, material selection for these patterns primarily affects visual comfort levels.

An Efficient Double Deep Q Learning Network-Based Soft Faults Detection and Localization in Analog Circuits

To identify whether the circuit under test is fault-free or faulty, fault diagnosis is conducted in an analog circuit. However, in the case of fault detection (FD) techniques, the size of FD is the main challenge in testing, especially for complex analog circuits. Hence to reduce the size of FD, specific test nodes are chosen to perform fault diagnosis from the available accessible test nodes. Specific test nodes are selected based on the faults classification efficiency of fault diagnosis. Therefore, this paper proposes an approach for single and multiple soft fault diagnosis using minimum test nodes in analog electronic circuits. The proposed double deep Q-learning-based hybrid Simulated annealing–Tabu search (DDQN-Hybrid SATS) technique determines minimum test nodes with the utilization of distance metric for fault classification. The DDQN-Hybrid SATS technique is used to identify minimum nodes for testing. In this, the double deep Q learning network (DDQN) ensures better reliability and faster convergence in learning but suffers from catastrophic forgetting issues. To prevent such issues, the DDQN approach is optimized using a hybrid SATS algorithm. The hybrid optimization of simulated annealing (SA) and Tabu search (TS) coalesce the advantages of individual optimization procedures to provide the optimum solution in a fast and effective way. The results for a fourth-order low pass filter are presented (i.e., first CUT) and an eight-bit digital to analog converter (i.e., second CUT). Moreover, the simulation experiment reveals that the proposed DDQN-Hybrid SATS technique achieves greater overall fault classification accuracy of about 96.25% than other compared techniques with minimum computation time.

Adaptive multi-strategy particle swarm optimization for solving NP-hard optimization problems

Particle Swarm Optimization algorithm (PSO) has been widely utilized for addressing optimization problems due to its straightforward implementation and efficiency in tackling various test functions and engineering optimization problems. Nevertheless, PSO encounters issues like premature convergence and a lack of diversity, particularly when confronted with complex high-dimensional optimization tasks. In this study, we propose an enhanced version of the Island Model Particle Swarm Optimization (IMPSO), where island models are integrated into the PSO algorithm based on several migration strategies. The first contribution consists in applying a new selection and replacement strategies based on tabu search technique, while the second contribution consists in proposing a dynamic migration rate relying on the Biogeography-Based Optimization technique. To assess and validate the effectiveness of the proposed method, several unconstrained benchmark functions are applied. The obtained results confirm that the approach yield better performance than the old version of IMPSO for solving NP-hard optimization problems. Compared to the performance of other well-known evolutionary algorithms, the proposed approach is more efficient and effective.

A hybridization of granular adaptive tabu search with path relinking for the multi-depot open vehicle routing problem

The multi-depot open vehicle routing problem (MDOVRP) differs from the classical VRP in that there is more than one depot and the vehicle does not need to return to a depot after serving the last customer. For solving this challenging problem, we propose a hybrid metaheuristic algorithm (GATS-PR) which integrates the granular adaptive tabu search with path relinking. The main contributions of this work consist of introducing a solution-based tabu search technique in granular tabu search, designing an adaptive neighborhood selection method for the large neighborhoods with 22 kinds of move types, and adopting path relinking with a new similarity definition to the MDOVRP for the first time. Computational results on 24 public instances demonstrate that GATS-PR outperforms the previous state-of-the-art algorithms in the literature. Specifically, GATS-PR improved and matched the previous best known results on 4 and 19 instances, respectively.

Hybrid packet routing algorithm based on ant colony system and tabu search in wireless sensor network

<span>Packet routing in wireless sensor network is one of the most crucial aspects as it controls the way packets move through sensor nodes with various capabilities to reach to the destination node. Inefficient routing process may lead to higher energy consumption, higher failure rate, and lower throughput. Metaheuristic algorithms have been some of the common approaches to solve these problems due to their adaptability with dynamic environment. This paper proposed a hybrid metaheuristics routing algorithm that hybridizes ant colony system and tabu search which focuses on exploitation and exploration mechanism while reducing the local optima. The proposed algorithm uses ant colony system technique to discover the best path for packet transmission by considering the energy level of each sensor node. Additionally, tabu search technique is applied to overcome the local optima problem by temporarily suspending the bad nodes and initiate backward movement with the aim to prevent the search agent from getting trapped in a blind alley. The proposed hybrid routing algorithm was evaluated against single and hybrid routing algorithms in terms of throughput, energy consumption, and energy efficiency. Experimental results showed that the proposed algorithm outperformed the other routing algorithms in terms of throughput, energy consumption, and energy efficiency.</span>

A multistart tabu search-based method for feature selection in medical applications

In the design of classification models, irrelevant or noisy features are often generated. In some cases, there may even be negative interactions among features. These weaknesses can degrade the performance of the models. Feature selection is a task that searches for a small subset of relevant features from the original set that generate the most efficient models possible. In addition to improving the efficiency of the models, feature selection confers other advantages, such as greater ease in the generation of the necessary data as well as clearer and more interpretable models. In the case of medical applications, feature selection may help to distinguish which characteristics, habits, and factors have the greatest impact on the onset of diseases. However, feature selection is a complex task due to the large number of possible solutions. In the last few years, methods based on different metaheuristic strategies, mainly evolutionary algorithms, have been proposed. The motivation of this work is to develop a method that outperforms previous methods, with the benefits that this implies especially in the medical field. More precisely, the present study proposes a simple method based on tabu search and multistart techniques. The proposed method was analyzed and compared to other methods by testing their performance on several medical databases. Specifically, eight databases belong to the well-known repository of the University of California in Irvine and one of our own design were used. In these computational tests, the proposed method outperformed other recent methods as gauged by various metrics and classifiers. The analyses were accompanied by statistical tests, the results of which showed that the superiority of our method is significant and therefore strengthened these conclusions. In short, the contribution of this work is the development of a method that, on the one hand, is based on different strategies than those used in recent methods, and on the other hand, improves the performance of these methods.

Optimization of the Residual Biomass Supply Chain: Process Characterization and Cost Analysis

This study delves into the critical role of logistical cost optimization in the residual woody biomass supply chain, aiming to enhance the sustainability and efficiency of this resource’s exploitation. The research underscores that proficient cost management of logistical operations is pivotal for the economic feasibility of residual biomass utilization. The paper scrutinizes key aspects, such as collection, transportation, storage, and processing of biomass, emphasizing their individual contributions to the overall cost. It also pays particular attention to the impacts of seasonality and biomass quality variations, which directly influence the cost and effectiveness of the supply chain. To facilitate a deeper understanding of these factors, the study introduces mathematical models that enable the exploration of diverse scenarios and optimization strategies. The use of linear programming, genetic algorithms, and tabu search techniques are discussed in the context of these models. The findings of this research hold significant implications for the management of the residual biomass supply chain and contribute to the transition towards a low-carbon economy.

Fuel loading pattern optimization of ALLEGRO fast reactor using the tabu search metaheuristic technique

In this work an improved tabu search (TS) optimization technique is used to find the optimal fuel loading pattern in the experimental fast reactor ALLEGRO. The penalty function method is used to solve this multi-objective combinatorial optimization problem. The objective function and constrains are computed via an interface between the developed optimizer and the ERANOS 2.3 deterministic code. To reduce the running time, the operating cycle simulation is carried out using the diffusion approximation and a 7-group neutron energy structure. The proposed objective function maximizes the k-eff value at the end of cycle, satisfying the maximum power factor, the excess hot reactivity, and the linear heat generation rate constraints. A novel procedure developed in ERANOS to perform fuel assembly swaps for neighbor search is presented. The algorithm performed as expected throughout the iterative process and, unlike other metaheuristics based on local search, did not get stuck in local optima, which is a well-known advantage of tabu search technique.

Energy-Efficient Routing Using Novel Optimization with Tabu Techniques for Wireless Sensor Network

Wireless Sensor Network (WSN) consists of a group of limited energy source sensors that are installed in a particular region to collect data from the environment. Designing the energy-efficient data collection methods in large-scale wireless sensor networks is considered to be a difficult area in the research. Sensor node clustering is a popular approach for WSN. Moreover, the sensor nodes are grouped to form clusters in a cluster-based WSN environment. The battery performance of the sensor nodes is likewise constrained. As a result, the energy efficiency of WSNs is critical. In specific, the energy usage is influenced by the loads on the sensor node as well as it ranges from the Base Station (BS). Therefore, energy efficiency and load balancing are very essential in WSN. In the proposed method, a novel Grey Wolf Improved Particle Swarm Optimization with Tabu Search Techniques (GW-IPSO-TS) was used. The selection of Cluster Heads (CHs) and routing path of every CH from the base station is enhanced by the proposed method. It provides the best routing path and increases the lifetime and energy efficiency of the network. End-to-end delay and packet loss rate have also been improved. The proposed GW-IPSO-TS method enhances the evaluation of alive nodes, dead nodes, network survival index, convergence rate, and standard deviation of sensor nodes. Compared to the existing algorithms, the proposed method outperforms better and improves the lifetime of the network.

Integer Factorization Problem Solving Using Tabu Search

Tabu Search (TS) technique is one way of search for goal algorithm in the Artificial Intelligent (AI). TS is used to solve several optimization problems. In this paper a proposed TS algorithm will be presented to solve the integer factorization problem that plays very big role in the cryptanalysis of public-key cryptography. The experimental result of this work shows that the proposed algorithm can factorize the numbers in good time compared with genetic algorithm technique.

Multi Swarm Optimization Based Clustering with Tabu Search in Wireless Sensor Network.

Wireless Sensor Networks (WSNs) can be defined as a cluster of sensors with a restricted power supply deployed in a specific area to gather environmental data. One of the most challenging areas of research is to design energy-efficient data gathering algorithms in large-scale WSNs, as each sensor node, in general, has limited energy resources. Literature review shows that with regards to energy saving, clustering-based techniques for data gathering are quite effective. Moreover, cluster head (CH) optimization is a non-deterministic polynomial (NP) hard problem. Both the lifespan of the network and its energy efficiency are improved by choosing the optimal path in routing. The technique put forth in this paper is based on multi swarm optimization (MSO) (i.e., multi-PSO) together with Tabu search (TS) techniques. Efficient CHs are chosen by the proposed system, which increases the optimization of routing and life of the network. The obtained results show that the MSO-Tabu approach has a 14%, 5%, 11%, and 4% higher number of clusters and a 20%, 6%, 14%, and 6% lesser average packet loss rate as compared to a genetic algorithm (GA), differential evolution (DE), Tabu, and MSO based clustering, respectively. Moreover, the MSO-Tabu approach has 136%, 36%, 136%, and 38% higher lifetime computation, and 22%, 16%, 51%, and 12% higher average dissipated energy. Thus, the study’s outcome shows that the proposed MSO-Tabu is efficient, as it enhances the number of clusters formed, average energy dissipated, lifetime computation, and there is a decrease in mean packet loss and end-to-end delay.

Efficient Hybrid Algorithm Solution for Optimal Reactive Power Flow Using the Sensitive Bus Approach

This paper presents the design and application of an efficient hybrid algorithm for solving the Optimal Reactive Power Flow (ORPF) problem. The ORPF is formulated as a nonlinear constrained optimization problem where the active power losses must be minimized. The proposed approach is based on the hybridization of Particle Swarm Optimization (PSO) and Tabu-Search (TS) technique. The proposed PSO-TS approach is used to find the settings of the control variables (i.e. generation bus voltages, transformer taps, and shunt capacitor sizes) which minimize transmission active power losses. The bus locations of the shunt capacitors are identified according to sensitive buses. To show the effectiveness of the proposed method, it is applied to the IEEE 30 bus benchmark test system and is compared with PSO and TS without hybridization, along with some other published approaches. The obtained results reveal the effectiveness of the proposed method in dealing with the highly nonlinear constrained nature of the ORPF problem.

Batch Assignment of Parallel Machines in an Automotive Safety Glass Manufacturing Facility

In this paper, optimization algorithms are used to solve a batch assignment problem of parallel processing furnaces in American Glass Products (AGP), a world leader company in the design and manufacturing of curved armored glass for transportation purposes. The problem consists in optimizing the bending process, which is considered to be the bottleneck workstation in the armored glasses production line in AGP. The objective is to maximize the efficiency of the furnaces and minimize the tardiness delivery of orders. Due to the complexity and constraints of the problem, we developed a proper dispatch algorithm and a Tabu search technique. The results are encouraging: the indicators of furnace usage hours and tardiness delivery improved by 32 % and 7 %, respectively compared to the decisions made in the plant during an actual production week. This work was the winner of an operation research challenge between around 100 graduate students. The challenge was organized by Javeriana University and AGP.

RETRACTED ARTICLE: Genetic algorithm-based tabu search for optimal energy-aware allocation of data center resources

Cloud computing delivers practical solutions for long-term image archiving systems. Cloud data centers consume enormous amounts of electrical energy that increases their operational costs. This shows the importance of investing on energy consumption techniques. Dynamic placement of virtual machines to appropriate physical nodes using metaheuristic algorithms is among the methods of reducing energy consumption. In metaheuristic algorithms, there should be a balance between both exploration and exploitation aspects so that they can find better solutions in a search space. Exploration means looking for a solution in a wider area, while exploitation is producing new solutions from existence ones. Artificial bee colony optimization, which is a biological metaheuristic algorithm, is a sign-oriented approach. It has a strong exploration ability, but a relatively weaker exploitation power. On the other hand, tabu search is a popular algorithm that shows better exploitation in comparison with ABC. In this study, cloud computing environments are detailed with an allocation protocol for efficient energy and resource management. The technique of energy-aware allocation splits data centers (DCs) resources among client applications end routes to enhance energy efficacy of DCs and also achieves anticipated quality of service (QoS) for everyone. Heuristic protocols are exercised for optimizing the distribution of resources to upgrade the efficiency of DC. In the current paper, energy-aware resources allotment technique is employed and optimized in clouds via a new approach called Tabu Job Master (JM). Tabu JM claims the benefits of some variables and also rapid convergence speeds. Results are duly achieved for energy consumption—the count of virtual machines (VMs) migration and also makespan. The results shown by Tabu JM are benchmarked by using genetic algorithm (GA), artificial bee colony (ABC), ABC with crossover and technique of mutation, the basic tabu search techniques, and Tabu Job Master.

A trilevel r-interdiction selective multi-depot vehicle routing problem with depot protection

The determination of critical facilities in supply chain networks has been attracting the interest of the Operations Research community. Critical facilities refer to structures including bridges, railways, train/metro stations, medical facilities, roads, warehouses, and power stations among others, which are vital to the functioning of the network. In this study we address a trilevel optimization problem for the protection of depots of utmost importance in a routing network against an intelligent adversary. We formulate the problem as a defender-attacker-defender game and refer to it as the trilevel r-interdiction selective multi-depot vehicle routing problem (3LRI-SMDVRP). The defender is the decision maker in the upper level problem (ULP) who picks u depots to protect among m existing ones. In the middle level problem (MLP), the attacker destroys r depots among the (m–u) unprotected ones to bring about the biggest disruption. Finally, in the lower level problem (LLP), the decision maker is again the defender who optimizes the vehicle routes and thereby selects which customers to visit and serve in the wake of the attack. All three levels have an identical objective function which is comprised of three components. (i) Operating or acquisition cost of the vehicles. (ii) Traveling cost incurred by the vehicles. (iii) Outsourcing cost due to unvisited customers. The defender aspires to minimize this objective function while the attacker tries to maximize it. As a solution approach to this trilevel discrete optimization problem, we resort to a smart exhaustive enumeration in the ULP and MLP. For the LLP we design a metaheuristic algorithm that hybridizes Variable Neighborhood Descent and Tabu Search techniques adapted to the Selective MDVRP (SMDVRP). The performance of this algorithm is demonstrated on 33 MDVRP benchmark instances existing in the literature and 41 SMDVRP instances generated from them. Numerical experiments on a large number of 3LRI-SMDVRP instances attest that our comprehensive method is effective in dealing with the defender-attacker-defender game on multi-depot routing networks.

A novel simulation-annealing enabled ranking and scaling statistical simulation constrained optimization algorithm for Internet-of-things (IoTs)

PurposeSimulation-based optimization is a decision-making tool for identifying an optimal design of a system. Here, optimal design means a smart system with sensing, computing and control capabilities with improved efficiency. As compared to testing the physical prototype, computer-based simulation provides much cheaper, faster and lesser time-and resource-consuming solutions. In this work, a comparative analysis of heuristic simulation optimization methods (genetic algorithms, evolutionary strategies, simulated annealing, tabu search and simplex search) is performed.Design/methodology/approachIn this work, a comparative analysis of heuristic simulation optimization methods (genertic algorithms, evolutionary strategies, simulated annealing, tabu search and simplex search) is performed. Further, a novel simulation annealing-based heuristic approach is proposed for critical infrastructure.FindingsA small scale network of 50–100 nodes shows that genetic simulation optimization with multi-criteria and multi-dimensional features performs better as compared to other simulation optimization approaches. Further, a minimum of 3.4 percent and maximum of 16.2 percent improvement is observed in faster route identification for small scale Internet-of-things (IoT) networks with simulation optimization constraints integrated model as compared to the traditional method.Originality/valueIn this work, simulation optimization techniques are applied for identifying optimized Quality of service (QoS) parameters for critical infrastructure which in turn helps in improving the network performance. In order to identify optimized parameters, Tabu search and ant-inspired heuristic optimization techniques are applied over QoS parameters. These optimized values are compared with every monitoring sensor point in the network. This comparative analysis helps in identifying underperforming and outperforming monitoring points. Further, QoS of these points can be improved by identifying their local optimum values which in turn increases the performance of overall network. In continuation, a simulation model of bus transport is taken for analysis. Bus transport system is a critical infrastructure for Dehradun. In this work, feasibility of electric recharging units alongside roads under different traffic conditions is checked using simulation. The simulation study is performed over five bus routes in a small scale IoT network.

Path Planning and Control of Mobile Robots Using Modified Tabu Search Algorithm in Complex Environment

An innovative modified Tabu search technique is developed for the effective navigation of mobile robots in complex environment. The method is modified to improve the efficiency of mobile robot towards having optimized path in short duration of time. Tabu search method works on the principle of getting shortest path by forming a Tabu list, which informs the mobile robot not to visit the previously visited positions and helps the robot in obtaining the obstacle negotiation angle. The modified Tabu search method helps the mobile robot to travel from any source to target location in any environment with suitable obstacle negotiation angle avoiding hurdles in its path. The developed modified algorithm has been incorporated in mobile robots and various trials were carried out in different environments to confirm the effectiveness of modified algorithm.

Using hybrid algorithm to reduce non-working distance in intra- and inter-field logistics simultaneously for heterogeneous harvesters

Reducing the non-working distance for agricultural vehicles is beneficial for reducing the total operational costs for farmers. In this study, we optimize the non-working distance in intra- and inter-fields simultaneously for harvesters while harvesting rice. A mixed integer programming is presented for optimizing the rice harvesting problem, where a similar working time for all harvesters are considered based on a real case. Besides, in this model, two kinds of fields, namely moist and non-moist fields are considered, where the first can only be harvested by crawler harvesters while the second can be harvested by any type of harvester. This model aims to find an optimal schedule for the harvesters. A hybrid algorithm using both adaptive large neighborhood search and tabu search techniques is implemented to optimize the problem. A case study is considered to verify the performance of the hybrid algorithm. The results indicate that the hybrid algorithm is an effective method for solving the rice harvesting problem because the total non-working distance could be improved significantly by 15.3%. The proportion of moist fields is analyzed because it influences the schedule significantly. When compared to the traditional method for optimizing the non-working distance for harvesters, the total non-working distance in this method could be reduced by 6.2%. This paper provides farmers with a general framework for reducing the non-working distance in intra- and inter-field logistics, which results in a more efficient performance of the harvesters in the fields.

Query Optimization : A Metaheuristics Approach Using Modified Memetics Algorithm (MMA)

The more complex business process of a system, the greater data that is stored. Increase of data transactions has an impact on a system performance. Therefore it is needed to optimize the query processing on data storage to maintain and improve the system performance. A memetics algorithm (MA) is a population-based metaheuristics approach which is the development of traditional genetics algorithms (GA) combined with local search (LS) technique. By using tabu search (TS) technique on the crossover operation in GA, this research proposes the modified memetics algorithm (MMA) for query optimization. The result shows that the processing time of the optimized (MMA) query is faster than the unoptimized query.

Tsunami-induced traffic evacuation strategy optimization

An optimal traffic evacuation strategy is of practical importance to minimize potential devastating damages caused by tsunamis. In this study, we developed a tsunami-induced, network-wide traffic evacuation optimization strategy. A hierarchical evacuation structure is established to guide vehicle routing decisions. At the first stage of evacuation, the optimization objective is to minimize the maximum evacuation time for all the individual evacuees from affected areas to temporary shelter zones to satisfy their primary safety needs within the shortest time periods. At the second stage of evacuation, the optimization objective aims to minimize the total evacuation time of all the evacuees from temporary shelter zones to safe zones. The lexicographic minimax optimization and Tabu search techniques are adopted to obtain a unique solution. Numerical examples are conducted to demonstrate the effectiveness of the developed model based on a real transportation network in Honolulu, Hawaii. The proposed evacuation strategy contributes to the state of the art by innovatively balancing the evacuation safety priority and the evacuation efficiency in the hierarchical evacuation structure. The research findings are helpful for decision makers to develop optimal tsunami-induced, network-wide traffic evacuation strategies.