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Imperial competitive algorithm with policy learning for the traveling salesman problem

SOFT COMPUTING 2017年第7期21卷 1863-1875页

作者： Chen, Meng-Hui Chen, Shih-Hsin Chang, Pei-Chann Yuan Ze Univ Dept Informat Management Innovat Ctr Big Data & Digital Convergence Chungli Taiwan Cheng Shiu Univ Dept Informat Management Kaohsiung Taiwan

The traveling salesman problem (TSP) is one of the most studied combinatorial optimization problems. In this paper, we present the new idea of combining the imperial competitive algorithm with a policy-learning function for solving the TSP problems. All offspring of each country are defined as representing feasible solutions for the TSP. All countries can grow increasingly strong by learning the effective policies of strong countries. Weak countries will generate increasingly excellent offspring by learning the policies of strong countries while retaining the characteristics of their own country. Imitating these policies will enable the weak countries to produce improved offspring;the solutions generated will, therefore, acquire a favorable scheme while maintaining diversity. Finally, experimental results for TSP instances from the TSP library have shown that our proposed algorithm can determine the salesman's tour with more effective performance levels than other known methods.

关键词： Traveling salesman problem Imperial competitive algorithm combinatorial optimization problems Artificial chromosomes Genetic algorithm

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EWSO: Boosting White Shark Optimizer for solving engineering design and combinatorial problems

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MATHEMATICS AND COMPUTERS IN SIMULATION 2024年 225卷 1124-1153页

作者： Houssein, Essam H. Saeed, Mahmoud Khalaf Al-Sayed, Mustafa M. Minia Univ Fac Comp & Informat Al Minya Egypt

Population-based meta-heuristic algorithms are crucial for solving optimization issues. One of these recent algorithms that is now believed to be promising metaheuristic algorithm is the White Shark Optimizer (WSO). Although it has produced a number of encouraging results, it has some certain downsides like other metaheuristic algorithms (MAs). Dropping into the local minimum optima and local solution zones, the uneven distribution of exploration and exploitation abilities, and the slow rate of convergence are some of these downsides. To fight those, two efficient mechanisms, i.e., Enhanced Solution Quality (ESQ) and Orthogonal Learning (OL), have been applied to develop an enhanced version of WSO called EWSO. The effectiveness of EWSO has been comprehensively evaluated using the IEEE CEC'2022 test suite. For further verification and achieving the principle of generality, the proposed algorithm has been used to provide good solutions for three engineering design issues (i.e., Gear train, Vertical deflection of an I beam, and the piston lever), for further applicability it has also been employed to solve two combinatorial optimization problems (i.e., bin packing problem (BPP) and quadratic assignment problems (QAP)). This effectiveness has been evaluated compared to the most recent and common metaheuristics, i.e., Kepler optimization Algorithm (KOA), Seagull optimization Algorithm (SOA), Spider Wasp Optimizer (SWO), and some well-known metaheuristic algorithms such as;Sine cosine Algorithm (SCA), Whale optimization Algorithm (WOA), and Trees Social Relations optimization (TSR), in addition to the original SWO. The experimental results and statistical measures confirm the effectiveness and reliability of the proposed algorithm (EWSO) in tackling real-world issues. It is able to overcome the previous drawbacks by providing the global optimum and preventing premature convergence through an increase in population diversity.

关键词： Metaheuristics White Shark Optimizer (WSO) IEEE CEC2022 test suite combinatorial optimization problems Engineering design problems Bin packing problem Quadratic assignment problems

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On Aligning Non-Order-Associated Binary Decision Diagrams

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INFORMS JOURNAL ON COMPUTING 2023年第5期35卷 910-928页

作者： Bochkarev, Alexey A. Smith, J. Cole Rheinland Pfalz Tech Univ Kaiserslautern Landau Dept Math D-67663 Kaiserslautern Germany Syracuse Univ Dept Elect Engn & Comp Sci Syracuse NY 13244 USA

Recent studies employ collections of binary decision diagrams (BDDs) to solve combinatorial optimization problems. This paper focuses on the problem of optimally aligning two BDDs, that is, transforming them to enforce a common order of variables while keeping the total size of the diagrams as small as possible. We address this problem, which is known to be NP-hard, by introducing and studying a simplified problem instead of working with the more complex original diagrams. We discuss some basic properties of the simplified problem, design a corresponding heuristic for the original problem, and show empirically that this approach yields good quality alignments while significantly reducing the complexity of intermediate diagram transformations. We highlight the practicality of this approach in the context of a variation of the uncapacitated facility location problem.

关键词： analysis of algorithms data structures binary decision diagrams combinatorial optimization problems

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Quantum Annealing: An Introduction and New Developments

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JOURNAL OF COMPUTATIONAL AND THEORETICAL NANOSCIENCE 2011年第6期8卷 963-971页

作者： Ohzeki, Masayuki Nishimori, Hidetoshi Tokyo Inst Technol Dept Phys Meguro Ku Tokyo 1528551 Japan Kyoto Univ Dept Syst Sci Sakyo Ku Kyoto 6068501 Japan

Quantum annealing is a generic algorithm using quantum-mechanical fluctuations to search for the solution of an optimization problem. The present paper first reviews the fundamentals of quantum annealing and then reports on preliminary results for an alternative method. The review part includes the relationship of quantum annealing with classical simulated annealing. We next propose a novel quantum algorithm which might be available for hard optimization problems by using a classical-quantum mapping as well as the Jarzynski equality introduced in nonequilibrium statistical physics.

关键词： Quantum Annealing Jarzynski Equality combinatorial optimization problems

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Multiple ant colonies algorithm based on colony level interactions

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IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 2000年第2期E83A卷 371-379页

作者： Kawamura, H Yamamoto, M Suzuki, K Ohuchi, A Hokkaido Univ Grad Sch Engn Sapporo Hokkaido 0608628 Japan

Recently, researchers in various fields have shown interest in the behavior of creatures from the viewpoint of adaptiveness and flexibility. Ants, known as social insects, exhibit collective behavior in performing tasks that can not be carried out by an individual ant. In ant colonies. chemical substances, called pheromones, are used as a way to communicate important information on global behavior. For example. ants looking for food lay the way back to their nest with a specific type of pheromone. Other ants can follow the pheromone trail and find their way to baits efficiently. In 1991, Colorni et al. proposed the ant algorithm for Traveling Salesman problems (TSPs) by using the analogy of such foraging behavior and pheromone communication. In the ant algorithm, there is a colony consisting of many simple ant agents that continuously visit TSP cities with opinions to prefer subtours connecting near cities and they lay strong pheromones. The ants completing their tours lay pheromones of various intensities with passed subtours according to distances. Namely, subtours in TSP tourns that have the possibility of being better tend to have strong pheromones, so the ant agents specify good regions in the search space by using this positive feedback mechanism. In this paper, we propose a multiple ant colonies algorithm that has been extended from the ant algorithm. This algorithm as several ant colonies for solving a TSP, while the original has only a single ant colony. Moreover, two kinds of pheromone effects, positive and negative pheromone effects, are introduced as the colony-level interactions. As a result of colony-level interactions, the colonies can exchange good schemata for solving a problem and can maintain their own variation in the search process. The proposed algorithm shows better performance than the original algorithm with almost the same agent strategy used in both algorithms except for the introduction of colony-level interactions.

关键词： multi-agent system ant algorithm traveling salesman problems combinatorial optimization problems

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Optimizing traveling salesman problem using tabu search metaheuristic algorithm with Pythagorean fuzzy uncertainty

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GRANULAR COMPUTING 2024年第1期9卷 1-29页

作者： Habib, Amna Akram, Muhammad Univ Punjab Dept Math New Campus Lahore Pakistan

The traveling salesman problem is a well-known combinatorial optimization problem. Solving the traveling salesman problem efficiently becomes more challenging when considering uncertainties in the problem parameters, which are prevalent in real-world scenarios. Pythagorean fuzzy uncertain variables combine the strengths of fuzzy logic with the principles of uncertainty theory, allowing for a more balanced and comprehensive representation of uncertainty. This paper defines the theoretical foundations of discrete, linear, and zigzag Pythagorean fuzzy uncertainty distributions, including their mathematical formulation and operational laws. Moreover, it proposes a novel approach to tackle the traveling salesman problem with Pythagorean fuzzy uncertainty distribution using the tabu search metaheuristic. By integrating this uncertainty representation into the tabu search metaheuristic, the proposed method effectively explores the solution space while considering the potential variations in the Pythagorean fuzzy distance matrix. The detailed steps of the algorithm are demonstrated via a numerical exemplification. Subsequently, a comprehensive case study is presented, wherein the objective is to determine the optimal touring sequence among the largest cities of China. This investigation is founded upon data source from Google Maps. We conduct a sensitivity analysis to assess the sensitivity of the model's output by varying the input parameters. The proposed algorithm's performance is compared with traditional tabu search and other existing methods. The results highlight the potential of incorporating Pythagorean fuzzy uncertainty distribution into metaheuristic algorithms for solving combinatorial optimization problems.

关键词： Pythagorean fuzzy uncertain variables combinatorial optimization problems Traveling salesperson problem Metaheuristic algorithms

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Random field models for fitness landscapes

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JOURNAL OF MATHEMATICAL BIOLOGY 1999年第5期38卷 435-478页

作者： Stadler, PF Happel, R Univ Vienna Inst Theoret Chem A-1090 Vienna Austria Santa Fe Inst Santa Fe NM 87501 USA

In many cases fitness landscapes are obtained as particular instances of random fields by randomly assigning a large number of parameters. Models of this type are often characterized reasonably well by their covariance matrices. We characterize isotropic random fields on finite graphs in terms of their Fourier series expansions and investigate the relation between the covariance matrix of the random field model and the correlation structure of the individual landscapes constructed from this random field. Correlation measures are a good characteristic of "rugged landscapes" models as they are closely related to quantities like the number of local optima or the length of adaptive walks. Our formalism suggests to approximate landscape with known autocorrelation function by a random field model that has the same correlation structure.

关键词： combinatorial optimization problems correlation function evolutionary optimization fitness landscapes Fourier series random fields

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Annealing Harmony Search Algorithm to Solve the Nurse Rostering Problem

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Computers, Materials & Continua 2022年第6期71卷 5545-5559页

作者： Mohammed Hadwan Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Computer Science College of Applied SciencesTaiz UniversityTaizYemen Intelligent Analytics Group(IAG) College of ComputerQassim UniversityBuraydahSaudi Arabia

A real-life problem is the rostering of nurses at *** is a famous nondeterministic,polynomial time(NP)-hard combinatorial optimization *** the real-world nurse rostering problem(NRP)constraints in distributing workload equally between available nurses is still a difficult task to *** international shortage of nurses,in addition to the spread of COVID-19,has made it more difficult to provide convenient rosters for *** on the literature,heuristic-based methods are the most commonly used methods to solve the NRP due to its computational complexity,especially for large ***-based algorithms in general have problems striking the balance between diversification and ***,this paper aims to introduce a novel metaheuristic hybridization that combines the enhanced harmony search algorithm(EHSA)with the simulated annealing(SA)algorithm called the annealing harmony search algorithm(AHSA).The AHSA is used to solve NRP from a Malaysian *** AHSA performance is compared to the EHSA,climbing harmony search algorithm(CHSA),deluge harmony search algorithm(DHSA),and harmony annealing search algorithm(HAS).The results show that the AHSA performs better than the other compared algorithms for all the tested instances where the best ever results reported for the UKMMC dataset.

关键词： Harmony search algorithm simulated annealing combinatorial optimization problems timetabling metaheuristic algorithms nurse rostering problems

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Efficient Scheduling of Multiple Mobile Chargers for Wireless Sensor Networks

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2016年第9期65卷 7670-7683页

作者： Xu, Wenzheng Liang, Weifa Lin, Xiaola Mao, Guoqiang Sichuan Univ Coll Comp Sci Chengdu 610065 Peoples R China Australian Natl Univ Res Sch Comp Sci Canberra ACT 0200 Australia Sun Yat Sen Univ Sch Informat Sci & Technol Guangzhou 51006 Guangdong Peoples R China Univ Technol Sydney Sch Comp & Commun Sydney NSW 2007 Australia

In this paper, we study the deployment of multiple mobile charging vehicles to charge sensors in a large-scale wireless sensor network for a given monitoring period so that none of the sensors will run out of energy, where sensors can be charged by the charging vehicles with wireless energy transfer. To minimize the network operational cost, we first formulate a charging scheduling problem of dispatching multiple mobile charging vehicles to collaboratively charge sensors such that the sum of travelling distance (referred to as the service cost) of these vehicles for this monitoring period is minimized, subject to that none of the sensors will run out of energy. Due to NP-hardness of the problem, we then propose a novel approximation algorithm with a guaranteed approximation ratio, assuming that the energy consumption rate of each sensor does not change for the given monitoring period. Otherwise, we devise a heuristic algorithm through modifications to the approximation algorithm. We finally evaluate the performance of the proposed algorithms via experimental simulations. Simulation results show that the proposed algorithms are very promising, which can reduce the service cost by up to 20% in comparison with the service costs delivered by existing ones.

关键词： Approximation algorithms combinatorial optimization problems mobile chargers periodic charging cycles rechargeable sensor networks wireless energy transfer

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An Enhanced Adaptive Large Neighborhood Search for Unrelated Parallel Machine Scheduling With Sequence Dependent Setup Times

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IEEE ACCESS 2023年 11卷 16735-16748页

作者： Ji, Bin Zhang, Suying Yu, Samson Shenglong Zhang, Dezhi Cent South Univ Sch Traff & Transportat Engn Changsha 410075 Peoples R China Deakin Univ Sch Engn Waurn Ponds Vic 3216 Australia

The unrelated parallel machine scheduling problem with sequence dependent setup times (UPMSP-SDST) addressed in this study refers to allocating jobs among a given number of machines and determining their processing sequence on each machine, to minimize the makespan (i.e., the maximum completion time). To deal with large-scale UPMSP-SDST with higher efficiency, this study presents an enhanced adaptive large neighborhood search (EALNS) algorithm with various destroy and repair operators and an efficiency-enhancement mechanism. The efficiency-enhancement mechanism is mainly composed of a simplified calculation method and a hierarchical comparison mechanism, which are applied to improve the implementation process of the greedy-based operators. The simplified calculation method obtains a new makespan by an incremental or decremental transformation, to avoid reluctant calculations. The hierarchical mechanism refines the comparison of different removal or insertion strategies of the operators, thereby arresting high-quality solutions with more metrics. The proposed algorithm is tested on 1640 instances, and numerical results demonstrate the superior performance of the EALNS over the existing methods, especially for large-scale problems. Notably, 834 instances' best-known solutions are updated from this study. In addition, deep analysis of the impact of the distribution of setup time on the performance of the algorithm is provided, which further verifies the potential wide applicability of the proposed EALNS.

关键词： Maintenance engineering Parallel machines Complexity theory Synchronization Standards Single machine scheduling Simulated annealing Adaptive large neighborhood search combinatorial optimization problems sequence-dependent setup time unrelated parallel machine scheduling

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