Swap Operator Research Articles

Hybrid Particle Swarm Optimization-Jaya Algorithm for Team Formation

Collaboration in a network is crucial for effective team formation. This paper addresses challenges in collaboration networks by identifying the skills required for effective team formation. The communication cost is low when agents with the same skills are connected. Our main objective is to minimize team communication costs by selecting agents with the required skills. However, finding an optimal team is a computationally hard problem. This study introduces a novel hybrid approach called I-PSO-Jaya (improved PSO-Jaya, which combines PSO (Particle Swarm Optimization) and the Jaya algorithm with the Modified Swap Operator to form efficient teams. A potential application scenario of the algorithm is to build a team of engineers for an IT project. The implementation results show that our approach gives an improvement of 73% in the Academia dataset and 92% in the ACM dataset compared to existing methods.

Relaxations and Exact Solutions to Quantum Max Cut via the Algebraic Structure of Swap Operators

The Quantum Max Cut (QMC) problem has emerged as a test-problem for designing approximation algorithms for local Hamiltonian problems. In this paper we attack this problem using the algebraic structure of QMC, in particular the relationship between the quantum max cut Hamiltonian and the representation theory of the symmetric group.The first major contribution of this paper is an extension of non-commutative Sum of Squares (ncSoS) optimization techniques to give a new hierarchy of relaxations to Quantum Max Cut. The hierarchy we present is based on optimizations over polynomials in the qubit swap operators. This is in contrast to the "standard" quantum Lasserre Hierarchy, which is based on polynomials expressed in terms of the Pauli matrices. To prove correctness of this hierarchy, we exploit a finite presentation of the algebra generated by the qubit swap operators. This presentation allows for the use of computer algebraic techniques to manipulate and simplify polynomials written in terms of the swap operators, and may be of independent interest. Surprisingly, we find that level-2 of this new hierarchy is numerically exact (up to tolerance 10−7) on all QMC instances with uniform edge weights on graphs with at most 8 vertices.The second major contribution of this paper is a polynomial-time algorithm that computes (in exact arithmetic) the maximum eigenvalue of the QMC Hamiltonian for certain graphs, including graphs that can be "decomposed" as a signed combination of cliques. A special case of the latter are complete bipartite graphs with uniform edge-weights, for which exact solutions are known from the work of Lieb and Mattis \cite{lieb1962ordering}. Our methods, which use representation theory of the symmetric group, can be seen as a generalization of the Lieb-Mattis result.

An efficient collaborative multi-swap iterated greedy algorithm for the distributed permutation flowshop scheduling problem with preventive maintenance

Under the context of globalization, distributed multi-factory production model is becoming the mainstream of the manufacturing industry because it provides enterprises with flexible and efficient production solutions. In real-world environments, the wear and tear of machines is unavoidable, so it is necessary to maintain equipment for sustainable processing and production. This paper explores the distributed permutation flowshop scheduling problem with preventive maintenance operations (DPFSP/PM) and introduces a collaborative multi-swap iterated greedy (CMSIG) algorithm to minimize the makespan. In the initialization phase, an improved heuristic algorithm is proposed, which re-swaps inserted jobs based on NEH2 to generate initial solutions. In the destruction phase, an adaptive destroy method is proposed. The factory is selected based on a tournament strategy and a critical factory strategy, and then the jobs are greedily reinserted in the construction phase. In the local search phase, four swap operators operate together to enhance their ability for exploring the solution space. Finally, extensive experiments show that the proposed CMSIG algorithm has significant advantages in solving DPFSP/PM compared with other algorithms.

Work Fluctuations in Ergotropic Heat Engines.

We study the work fluctuations in ergotropic heat engines, namely two-stroke quantum Otto engines where the work stroke is designed to extract the ergotropy (the maximum amount of work by a cyclic unitary evolution) from a couple of quantum systems at canonical equilibrium at two different temperatures, whereas the heat stroke thermalizes back the systems to their respective reservoirs. We provide an exhaustive study for the case of two qutrits whose energy levels are equally spaced at two different frequencies by deriving the complete work statistics. By varying the values of temperatures and frequencies, only three kinds of optimal unitary strokes are found: the swap operator U1, an idle swap U2 (where one of the qutrits is regarded as an effective qubit), and a non-trivial permutation of energy eigenstates U3, which indeed corresponds to the composition of the two previous unitaries, namely U3=U2U1. While U1 and U2 are Hermitian (and hence involutions), U3 is not. This point has an impact on the thermodynamic uncertainty relations (TURs), which bound the signal-to-noise ratio of the extracted work in terms of the entropy production. In fact, we show that all TURs derived from a strong detailed fluctuation theorem are violated by the transformation U3.

Historical information based iterated greedy algorithm for distributed flowshop group scheduling problem with sequence-dependent setup times

Distributed flowshop group scheduling problem (DFGSP) is commonly seen in modern industry. However, research works on DFGSP with total flow time criterion are rarely reported. The DFGSP consists of three coupled sub-problems, i.e., factory assignment for each group, group sequence in each factory, and job sequence within each group. A historical information-based iterated greedy algorithm (HIG) is proposed for solving the DFGSP with the objective of minimizing total flow time. The HIG integrates an iterated greedy algorithm (IG) with a group-based insertion operator, a job-based insertion operator, a domination criterion-based swap operator, and a historical information-based constructive solution method. The domination criterion is an effective inequality, which can deterministically optimize the objective value of a partial sequence even if the scheduling sequence of subsequent jobs is unknown. In the constructive solution method, a set covering model is designed to capture the effective factory allocation patterns for the groups hidden in the historical solutions to speed up the search for the IG. The comprehensive experiments on 810 test instances demonstrate the effectiveness of HIG.

Optimising Routes Scheduling of Garbage Truck for Waste Collection using Artificial Bee Colony Algorithm

The issue of waste generation has attracted significant public attention in today’s societies, not only because waste production is increasing quantitatively but also because some products and components are becoming more complex. The growing problem of municipal solid waste management has become the most serious environmental issue. Population growth has resulted in the increase in the generation of solid waste, which has become a serious issue that must be resolved. Optimised route planning is one of the most important components of a smart waste management system. Route planning is a technological approach to determining the shortest routes through optimisation. The route optimisation problem commonly referred to as the vehicle routing problem (VRP), has been thoroughly studied to find the best possible route for a vehicle. This study aims to find the optimal routes and reduce the distance travelled by the garbage truck when collecting waste using Artificial Bee Colony algorithm. ABC algorithm is an optimization method based on the clever behaviour of honey bee swarms. The artificial bee colony in the ABC algorithm is divided into three groups which are employed bees, onlookers and scouts. To provide neighbouring solutions that are guaranteed to be possible, swap operator is used. Results show that by using 10 trucks in total as the vehicle, the optimal solution produced a total of optimal distance for 94.9542 km. The findings of this study fulfilled the intended objectives which are to find the optimal route and shortest distance for the vehicles.

An Improved Binary Quadratic Interpolation Optimization for 0-1 Knapsack Problems

This paper presents a new binary optimization technique for solving the 0–1 knapsack problem. This algorithm is based on converting the continuous search space of the recently proposed quadratic interpolation optimization (QIO) into discrete search space using various V-shaped and S-shaped transfer functions; this algorithm is abbreviated as BQIO. To further improve its performance, it is effectively integrated with a uniform crossover operator and a swap operator to explore the discrete binary search space more effectively. This improved variant is called BIQIO. Both BQIO and BIQIO are assessed using 20 well-known knapsack instances and compared to four recently published metaheuristic algorithms to reveal their effectiveness. The comparison among algorithms is based on three performance metrics: the mean fitness value, Friedman mean rank and computational cost. The first two metrics are used to observe the accuracy of the results, while the last metric is employed to show the efficiency of each algorithm. The results of this comparison reveal the superiority of BIQIO over the classical BQIO and four rival optimizers.

Application and planning of an energy-oriented stochastic disassembly line balancing problem.

End-of-life (EOL) products are getting more and more attention as a result of the rapid decline in environmental resources and the dramatic rise in population at the moment. Disassembly is a crucial step in the reuse of EOL products. However, the disassembly process for EOL products is highly uncertain, and the disassembly planning method may not produce the anticipated outcomes in actual implementation. Based on the physical nature of the product disassembly process with multiple uncertain variables, certainty disassembly cannot adequately characterize the uncertain variables effectively. Uncertainty disassembly takes into account the changes in parts caused by product use, such as wear and corrosion, which can better coordinate the arrangement of disassembly tasks and better match the actual remanufacturing process. After analysis, it was found that most of studies on uncertain disassembly focus on the economic efficiency perspective and lack of energy consumption considerations. For the gaps in the current study, this paper proposes a stochastic energy consumption disassembly line balance problem (SEDLBP) and constructs a mathematical model of SEDLBP based on the disassembly of spatial interference matrix, In this model, the energy consumption generated by the disassembly operation and workstation standby is not a constant value but is generated stochastically in a uniformly distributed interval. In addition, an improved social engineering optimization algorithm that incorporates stochastic simulation (SSEO) is proposed in this paper to effectively address the issue. The incorporation of swap operators and swap sequences in SSEO makes it possible to solve discrete optimization problems efficiently. A comparison of a case study with some well-tested intelligent algorithms demonstrates the efficacy of the solutions produced by the proposed SSEO.

Swap Operator based Grey Wolf Optimizer for Materialized View Selection Problem

Swap Operator based Grey Wolf Optimizer for Materialized View Selection Problem

A binary tree seed algorithm with selection-based local search mechanism for huge-sized optimization problems

Tree seed algorithm (TSA) is a recently proposed metaheuristic algorithm for solving continuous optimization problems. In order to use TSA in binary optimization problems, the SimLogicTSA method was developed by adding logic gates and Jaccard’s similarity measure to this algorithm by Cinar and Kiran. Although SimLogicTSA is generally successful in small, medium, and large size problems, it has not been successful in the huge-sized problems by stucking into local minima. To overcome this problem, a new local search mechanism called enhanced local search module (ELSM) is proposed and the SimLogicTSA-ELSM algorithm is suggested by implementing the ELSM mechanism to the original SimLogicTSA algorithm. The proposed ELSM mechanism consists of a swap operator and logic-based gates. To analyze the contribution of the ELSM mechanism to the algorithm, firstly, the original SimLogicTSA and SimLogicTSA-ELSM algorithms were compared on the Cap and M* problem sets. The obtained results showed that the proposed algorithm produced more successful results than the original SimLogicTSA. Then, the proposed SimLogicTSA-ELSM is compared with many state-of-art algorithms in the literature by using different performance metrics on Cap and M* problem sets. The results show that SimLogicTSA-ELSM outperforms the compared algorithms in nearly all cases. Especially, the performance of the SimLogicTSA-ELSM stands out in huge-sized problems.

An Effective Iterated Greedy Algorithm for a Robust Distributed Permutation Flowshop Problem With Carryover Sequence-Dependent Setup Time

A new scheduling problem, the distributed permutation flowshop scheduling problem with uncertain processing times and carryover sequence-dependent setup time (DPUC), is addressed. The DPUC is an important application problem in modern electronics manufacturing. A robust model is established for the DPUC with makespan criterion. A counter-intuitive paradox is found, that is, adding a new job to one of the production lines can reduce the completion time of the production line. Two acceleration methods are provided to save computational efforts. An iterated greedy algorithm called IG_FS is proposed to solve the DPUC. A heuristic based on the well-known NEH is proposed to generate the initial solution for the IG_FS. In the destruction phase of the IG_FS, dynamic sizes based on both adaptability and randomness are provided to improve the exploration capability. During the local search phase of the IG_FS, a hybrid local search method consisting of shift and swap operators is presented to exploit more diverse search areas. Extensive experiments show that the proposed IG_FS performs significantly better than the six competing algorithms adapted from the closely related scheduling literature.

Pencil-beam Delivery Pattern Optimization Increases Dose Rate for Stereotactic FLASH Proton Therapy

FLASH dose rates >40 Gy/s are readily available in proton therapy (PT) with cyclotron-accelerated beams and pencil-beam scanning (PBS). The PBS delivery pattern will affect the local dose rate, as quantified by the PBS dose rate (PBS-DR), and therefore needs to be accounted for in FLASH-PT with PBS, but it is not yet clear how. Our aim was to optimize patient-specific scan patterns for stereotactic FLASH-PT of early-stage lung cancer and lung metastases, maximizing the volume irradiated with PBS-DR >40 Gy/s of the organs at risk voxels irradiated to >8 Gy (FLASH coverage). Plans to 54 Gy/3 fractions with 3 equiangular coplanar 244 MeV proton shoot-through transmission beams for 20 patients were optimized with in-house developed software. Planning target volume-based planning with a 5 mm margin was used. Planning target volume ranged from 4.4 to 84 cc. Scan-pattern optimization was performed with a Genetic Algorithm, run in parallel for 20 independent populations (islands). Mapped crossover, inversion, swap, and shift operators were applied to achieve (local) optimality on each island, with migration between them for global optimality. The cost function was chosen to maximize the FLASH coverage per beam at >8 Gy, >40 Gy/s, and 40 nA beam current. The optimized patterns were evaluated on FLASH coverage, PBS-DR distribution, and population PBS-DR-volume histograms, compared with standard line-by-line scanning. Robustness against beam current variation was investigated. The optimized patterns have a snowflake-like structure, combined with outward swirling for larger targets. A population median FLASH coverage of 29.0% was obtained for optimized patterns compared with 6.9% for standard patterns, illustrating a significant increase in FLASH coverage for optimized patterns. For beam current variations of 5 nA, FLASH coverage varied between -6.1%-point and 2.2%-point for optimized patterns. Significant improvements on the PBS-DR and, hence, on FLASH coverage and potential healthy-tissue sparing are obtained by sequential scan-pattern optimization. The optimizer is flexible and may be further fine-tuned, based on the exact conditions for FLASH.

Refined descriptive sampling simulated annealing algorithm for solving the traveling salesman problem

Abstract The simulated annealing (SA) algorithm is a popular intelligent optimization algorithm which has been successfully applied in many fields. In this paper, we propose a software component under the Windows environment called goRDS which implements a refined descriptive sampling (RDS) number generator of high quality in the MATLAB programming language. The aim of this generator is to sample random inputs through the RDS method to be used in the Simple SA algorithm with swap operator. In this way, the new probabilistic meta-heuristic algorithm called RDS-SA algorithm will enhance the simple SA algorithm with swap operator, the SA algorithm and possibly its variants with solutions of better quality and precision. Towards this goal, the goRDS generator was highly tested by adequate statistical tests and compared statistically to the random number generator (RNG) of MATLAB, and it was proved that goRDS has passed all tests better. Simulation experiments were carried out on the benchmark traveling salesman problem (TSP) and the results show that the solutions obtained with the RDS-SA algorithm are of better quality and precision than those of the simple SA algorithm with swap operator, since the software component goRDS represents the probability behavior of the SA input random variables better than the usual RNG.

A collaborative iterative greedy algorithm for the scheduling of distributed heterogeneous hybrid flow shop with blocking constraints

The hybrid flow shop and distributed flow shop problems have been extensively studied due to their wide industrial applications. However, the distributed heterogeneous hybrid flow shop problems (DHHFSP) with blocking constraints have not yet been well studied up to date. This paper considers how to arrange a variety of jobs to different heterogeneous factories, and each factory has a minimal makespan. The innovations of this paper lie in presenting a mathematical model of the DHHFSP with blocking constraints and designing a collaborative iterative greedy (CIG) algorithm. The CIG contains the problem-specific initialization strategy, the neighborhood search strategy, the destruction-reconstruction strategy, and the local intensification strategy. The cross-factory and inner-factory neighborhood search strategies based on two swap operators are adopted to reduce the blocking time. The local intensification strategy is developed to optimize the scheduling sequence of each factory. The proposed algorithm is empirically compared with five state-of-the-art algorithms on 60 different instance sets. The experimental results show that the proposed algorithm significantly outperforms the compared ones in terms of objective values and relative percentage deviation values.

An improved iterated greedy algorithm for the energy-efficient blocking hybrid flow shop scheduling problem

• A mathematical model of a blocking hybrid flow shop scheduling problem with energy consumption criteria is proposed. • A simple global perturbation based on a half-swap operator is proposed to further search for the potentially best solution. • A local perturbation strategy based on a swap operator is designed to ensure the convergence of the algorithm. • The proposed algorithm is applied to 140 test instances with different scales and compared with state-of-the-art algorithms and can obtain the best solution to the BHFSP. With the continuous development of national economies, problems of various energy consumption levels and pollution emissions in manufacturing have attracted attention from researchers. Most existing research has focused on reducing economic costs and energy consumption. However, the Hybrid Flow Shop Scheduling Problem with energy-efficient criteria has not yet been well studied, especially with blocking constraints. This paper is the first to present a mathematical model of the blocking hybrid flow shop problem with an energy-efficient criterion and a modified Iterative Greedy algorithm based on a swap strategy designed to optimize the constructed model. In the proposed algorithm, first, a heuristic is adopted to generate the initial solution. Second, a local perturbation strategy based on a swap operator is designed to ensure the convergence of the algorithm. Third, a simple global perturbation strategy based on a half-swap operator is proposed as a means to further search for the potentially best solution with the traditional simulated annealing criterion. The proposed algorithm is applied to 150 test instances at different scales and compared to state-of-the-art algorithms. The experimental results demonstrate that the proposed algorithm outperforms the compared algorithms and can obtain a better solution.

A hash map-based memetic algorithm for the distributed permutation flowshop scheduling problem with preventive maintenance to minimize total flowtime

Distributed scheduling problems have been an active research topic due to their close connection with the multifactory production environment in today’s decentralized economy. In this paper, the distributed permutation flowshop scheduling problem with preventive maintenance is studied with the goal of minimizing the total flowtime. A mathematical model and a hash map-based algorithm are presented to tackle the problem. The Nawaz–Enscore–Ham heuristic is improved and incorporated with the distributed Liu–Reeves heuristic to provide a promising initial solution in a finite number of steps. The proposed algorithm employs the data structure of the hash map to store all the candidate solutions in the form of key–value pairs. The selection, crossover, and mutation operators are also modified to expand the scope of exploration in the discrete domain. The local search hybridizes the job insertion operator and the job swap operator to further improve the obtained offspring solutions and utilizes the characteristic of the population hash map to reduce the efforts of solution evaluations. The candidate solution obtained by the local search is hashed by a rotating hash method and then placed at the node with the corresponding hash code. A series of experiments were conducted to verify the effectiveness of the hybrid local search operators and hash map strategy. Computational results indicate that the multiple strategies not only help to escape from the local optimization but also improve the computational efficiency. When compared with 10 state-of-the-art algorithms, the proposed algorithm generates an average relative percentage deviation of 0.134%, which is significant improvement.

Comparison of a Bat and Genetic Algorithm Generated Sequence Against Lead Through Programming When Assembling a PCB Using a Six-Axis Robot With Multiple Motions and Speeds

An optimal component feeder arrangement and robotic placement sequence are both important for improving assembly efficiency. Both problems are combinatorial in nature and known to be NP-hard. This article presents a novel discrete hybrid bat-inspired algorithm for solving the feeder slot assignment and placement sequence problem encountered when planning robotic assembly of electronic components. In our method, we use the concepts of swap operators and swap sequence to redefine position and velocity operators from the basic bat algorithm (BA). Furthermore, we propose an improved local search method based on genetic operators of crossover and mutation enhanced by the 2-opt search procedure. The algorithm is formulated with the objective of minimizing the total traveling distance of the pick-and-place device. Through numerical experiments, using a real printed circuit board (PCB) assembly scenario, we demonstrate the considerable effectiveness of the proposed discrete BA to improve the selection of feeder arrangement and placement sequence in PCB assembly operations and achieve high throughput production. The results also highlighted that even though the algorithms outperformed traditional lead through programming techniques, the programmer must consider the influence of different robot motions and speeds.

Discrete sparrow search algorithm for symmetric traveling salesman problem

The traveling salesman problem (TSP) is one of the most intensively studied problems in computational mathematics. This paper proposes a swarm intelligence approach using a discrete sparrow search algorithm (DSSA) with a global perturbation strategy to solve the problem. Firstly, the initial solution in the population is generated by the roulette-wheel selection. Secondly, the order-based decoding method is introduced to complete the update of the sparrow position. Then, the global perturbation mechanism combined with Gaussian mutation and swap operator is adopted to balance exploration and exploitation capability. Finally, the 2-opt local search is integrated to further improve the quality of the solution. Those strategies enhance the solution’s quality and accelerate the convergence. Experiments on 34 TSP benchmark datasets are conducted to investigate the performance of the proposed DSSA. And statistical tests are used to verify the significant differences between the proposed DSSA and other state-of-the-art methods. Results show that the proposed method is more competitive and robust in solving the TSP.

Evaluation and Selection of Battery Swap Operation Mode of New Energy Vehicles

With the advantages of "vehicle-electricity separation", effectively shortening the replenishment time, relieving users' mileage anxiety, and facilitating the perfection of power battery recycling system, new energy vehicle battery swap has become an important research direction to respond to the government's "low carbon" policy and promote the development of new energy vehicle industry. In this paper, we analyze the differences in the supply chain structure of the three battery swap models led by car manufacturers, battery manufacturers and battery swap operators, and build models of battery swap operation under three different power structures based on the actual market situation. Using the combination of Analytic Hierarchy Process (AHP) and Fuzzy Comprehensive Evaluation (FCE), we use the questionnaires and expert evaluation methods to evaluate and analyze the input and output of the battery swap enterprises, supply chain risk resilience and competitiveness of new energy vehicles under the three different supply chain structures. We compare and conclude that the battery swap model dominated by battery enterprises is generally better than the other two battery swap operation models, then we analyze and conclude the advantages of different operation modes in order to draw some management insights.

An Improved Flower Pollination Algorithm for Global and Local Optimization

Meta-heuristic algorithms have emerged as a powerful optimization tool for handling non-smooth complex optimization problems and also to address engineering and medical issues. However, the traditional methods face difficulty in tackling the multimodal non-linear optimization problems within the vast search space. In this paper, the Flower Pollination Algorithm has been improved using Dynamic switch probability to enhance the balance between exploitation and exploration for increasing its search ability, and the swap operator is used to diversify the population, which will increase the exploitation in getting the optimum solution. The performance of the improved algorithm has investigated on benchmark mathematical functions, and the results have been compared with the Standard Flower pollination Algorithm (SFPA), Genetic Algorithm, Bat Algorithm, Simulated annealing, Firefly Algorithm and Modified flower pollination algorithm. The ranking of the algorithms proves that our proposed algorithm IFPDSO has outperformed the above-discussed nature-inspired heuristic algorithms.