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Автор: Grafiati
Опубліковано: 22 червня 2024

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  1. Статті в журналах
  2. Дисертації
  3. Частини книг
  4. Тези доповідей конференцій

Статті в журналах з теми "Encodings and local search operators":

1

Raidl, Günther R., and Jens Gottlieb. "Empirical Analysis of Locality, Heritability and Heuristic Bias in Evolutionary Algorithms: A Case Study for the Multidimensional Knapsack Problem." Evolutionary Computation 13, no. 4 (December 2005): 441–75. http://dx.doi.org/10.1162/106365605774666886.

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Our main aim is to provide guidelines and practical help for the design of appropriate representations and operators for evolutionary algorithms (EAs). For this purpose, we propose techniques to obtain a better understanding of various effects in the interplay of the representation and the operators. We study six different representations and associated variation operators in the context of a steady-state evolutionary algorithm for the multidimensional knapsack problem. Four of them are indirect decoder-based techniques, and two are direct encodings combined with different initialization, repair, and local improvement strategies. The complex decoders and the local improvement and repair strategies make it practically impossible to completely analyze such EAs in a fully theoretical way. After comparing the general performance of the chosen EA variants for the multidimensional knapsack problem on two benchmark suites, we present a hands-on approach for empirically analyzing important aspects of initialization, mutation, and crossover in an isolated fashion. Static, inexpensive measurements based on randomly created solutions are performed in order to quantify and visualize specific properties with respect to heuristic bias, locality, and heritability. These tests shed light onto the complex behavior of such EAs and point out reasons for good or bad performance. In addition, the proposed measures are also examined during actual EA runs, which gives further insight into dynamic aspects of evolutionary search and verifies the validity of the isolated static measurements. All measurements are described in a general way, allowing for an easy adaption to other representations and problems.
2

Varnamkhasti, M. Jalali. "A genetic algorithm rooted in integer encoding and fuzzy controller." IAES International Journal of Robotics and Automation (IJRA) 8, no. 2 (June 1, 2019): 113. http://dx.doi.org/10.11591/ijra.v8i2.pp113-124.

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The premature convergence is the essential problem in genetic algorithms and it is strongly related to the loss of genetic diversity of the population. In this study, a new sexual selection mechanism which utilizing mate chromosome during selection proposed and then technique focuses on selecting and controlling the genetic operators by applying the fuzzy logic controller. Computational experiments are conducted on the proposed techniques and the results are compared with some other operators, heuristic and local search algorithms commonly used for solving benchmark problems published in the literature.
3

Gao, Yilong, Zhiqiang Xie, Xinyang Liu, Wei Zhou, and Xu Yu. "Integrated scheduling algorithm based on the priority constraint table for complex products with tree structure." Advances in Mechanical Engineering 12, no. 12 (December 2020): 168781402098520. http://dx.doi.org/10.1177/1687814020985206.

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Aiming at the existing intelligent optimization algorithms for solving the integrated scheduling problem of complex products with tree structure, there are problems of missing optimal solutions when designing encoding methods or generating infeasible offspring while designing evolutionary operators, an integrated scheduling algorithm based on the priority constraint table is proposed in this paper. A novel encoding method based on the dynamic priority constraint table is developed, which can guarantee the feasibility and completeness of the initial population individuals. For the legitimacy of the generated offspring individuals, two new different crossover and mutation methods are designed separately. The introduced evolutionary operators can avoid the detection and repairment of the infeasible individuals. An insertion-based greedy decoding method is also developed. In addition, based on the critical operations, a local search strategy is presented to enhance the search ability for the superior solutions. The feasibility and superiority of the proposed algorithm is verified by comparative experiments.
4

Jalali Varnamkhasti, M., and L. S. Lee. "A Fuzzy Genetic Algorithm Based on Binary Encoding for Solving Multidimensional Knapsack Problems." Journal of Applied Mathematics 2012 (2012): 1–23. http://dx.doi.org/10.1155/2012/703601.

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The fundamental problem in genetic algorithms is premature convergence, and it is strongly related to the loss of genetic diversity of the population. This study aims at proposing some techniques to tackle the premature convergence by controlling the population diversity. Firstly, a sexual selection mechanism which utilizes the mate chromosome during selection is used. The second technique focuses on controlling the genetic parameters by applying the fuzzy logic controller. Computational experiments are conducted on the proposed techniques and the results are compared with other genetic operators, heuristics, and local search algorithms commonly used for solving multidimensional 0/1 knapsack problems published in the literature.
5

Jiang, Tianhua. "A Hybrid Grey Wolf Optimization for Job Shop Scheduling Problem." International Journal of Computational Intelligence and Applications 17, no. 03 (September 2018): 1850016. http://dx.doi.org/10.1142/s1469026818500165.

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This paper aims to develop a hybrid grey wolf optimization algorithm (HGWO) for solving the job shop scheduling problem (JSP) with the objective of minimizing the makespan. Firstly, to make the GWO suitable for the discrete nature of JSP, an encoding mechanism is proposed to implement the continuous encoding of the discrete scheduling problem, and a ranked-order value (ROV) rule is used to conduct the conversion between individual position and operation permutation. Secondly, a heuristic algorithm and the random rule are combined to implement the population initialization in order to ensure the quality and diversity of initial solutions. Thirdly, a variable neighborhood search algorithm is embedded to improve the local search ability of our algorithm. In addition, to further improve the solution quality, genetic operators (crossover and mutation) are introduced to balance the exploitation and exploration ability. Finally, experimental results demonstrate the effectiveness of the proposed algorithm based on 23 benchmark instances.
6

Yang, Jinfeng, and Hua Xu. "Hybrid Memetic Algorithm to Solve Multiobjective Distributed Fuzzy Flexible Job Shop Scheduling Problem with Transfer." Processes 10, no. 8 (August 1, 2022): 1517. http://dx.doi.org/10.3390/pr10081517.

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Most studies on distributed flexible job shop scheduling problem (DFJSP) assume that both processing time and transmission time are crisp values. However, due to the complexity of the factory processing environment, the processing information is uncertain. Therefore, we consider the uncertainty of processing environment, and for the first time propose a multiobjective distributed fuzzy flexible job shop scheduling problem with transfer (MO-DFFJSPT). To solve the MO-DFFJSPT, a hybrid decomposition variable neighborhood memetic algorithm (HDVMA) is proposed with the objectives of minimizing the makespan, maximum factory load, and total workload. In the proposed HDVMA, the well-designed encoding/decoding method and four initialization rules are used to generate the initial population, and several effective evolutionary operators are designed to update populations. Additionally, a weight vector is introduced to design high quality individual selection rules and acceptance criteria. Then, three excellent local search operators are designed for variable neighborhood search (VNS) to enhance its exploitation capability. Finally, a Taguchi experiment is designed to adjust the important parameters. Fifteen benchmarks are constructed, and the HDVMA is compared with four other famous algorithms on three metrics. The experimental results show that HDVMA is superior to the other four algorithms in terms of convergence and uniformity of non-dominated solution set distribution.
7

Weise, Thomas. "Software - motipy: the Metaheuristic Optimization in Python Library." ACM SIGEVOlution 16, no. 4 (December 2023): 1–2. http://dx.doi.org/10.1145/3638461.3638464.

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We are thankful for the opportunity to announce our Python package "moptipy," which offers a rich set of tools for implementing, applying, and experimenting with metaheuristic optimization algorithms. Our package has been designed with benchmarking in mind and thus provides very comprehensive experimentation, data collection, and evaluation facilities out of the box. moptipy has the following features: 1. Very comprehensive documentation with many examples, reaching down to literature references inside the code and up to complex example experiments. 2. Several standard search spaces (bit strings, permutations, real vectors), operators, and algorithms, including randomized local search/hill climbers, simulated annealing, evolutionary algorithms, memetic algorithms, NSGA-II, several numerical optimization algorithms, etc., are already implemented and ready for use. 3. It is very easy to implement new algorithms using moptipy, be they general black-box methods or tailored for specific optimization problems. 4. It is also easy to integrate algorithms from external libraries and unify them under our API, which we did as proof-of-concept with CMA-ES, BOBYQA, as well as for the algorithms from SciPy. 5. Data can be collected at different verbosity levels, ranging from only providing the final result and its quality via the API (without creating any files) to creating log files with all (or all improving) steps of an algorithm, the result, the algorithm and problem parameters, the system setup, non-dominated solutions, and the random seed for fully-reproducible experiments. [1] 6. An experiment execution facility for very simple and robust parallel and distributed experimentation is provided. Parallelization and distribution works out-of-the-box based on shared folders and thus does not require additional libraries or programming effort [1]. 7. Stopping criteria for optimization processes can be defined based on goal solution qualities, clock time, and/or consumed objective function evaluations. 8. All experiments are fully reproducible, i.e., from a log file an algorithm and problem can be configured such that, in the replication experiment, exactly the same search steps are performed as in the original setting [1]. 9. The experiment evaluation facility can parse the log files and generate progress plots, result tables, ERT and ECDF plots, statistical test tables, and export data towards Excel or the popular IOHanalyzer. 10. Both single-objective and multi-objective optimization are supported under the same unified API. 11. The package has a good unit test coverage and pre-defined tools to unit test your own code. When implementing new objective functions, algorithms, encodings, oder operators, it is possible to use these tools to look for errors.
8

Zhang, L., and U. Kleine. "A novel bottom-left packing genetic algorithm for analog module placement." Advances in Radio Science 1 (May 5, 2003): 191–96. http://dx.doi.org/10.5194/ars-1-191-2003.

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Abstract. This paper presents a novel genetic algorithm for analog module placement. It is based on a generalization of the two-dimensional bin packing problem. The genetic encoding and operators assures that all constraints of the problem are always satisfied. Thus the potential problems of adding penalty terms to the cost function are eliminated, so that the search configuration space decreases drastically. The dedicated cost function covers the special requirements of analog integrated circuits. A fractional factorial experiment was conducted using an orthogonal array to study the algorithm parameters. A meta-GA was applied to determine the optimal parameter values. The algorithm has been tested with several local benchmark circuits. The experimental results show this promising algorithm makes the better performance than simulated annealing approach with the satisfactory results comparable to manual placement.
9

Salcedo-Sanz, S., J. Del Ser, and Z. W. Geem. "An Island Grouping Genetic Algorithm for Fuzzy Partitioning Problems." Scientific World Journal 2014 (2014): 1–15. http://dx.doi.org/10.1155/2014/916371.

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This paper presents a novel fuzzy clustering technique based on grouping genetic algorithms (GGAs), which are a class of evolutionary algorithms especially modified to tackle grouping problems. Our approach hinges on a GGA devised for fuzzy clustering by means of a novel encoding of individuals (containing elements and clusters sections), a new fitness function (a superior modification of the Davies Bouldin index), specially tailored crossover and mutation operators, and the use of a scheme based on a local search and a parallelization process, inspired from an island-based model of evolution. The overall performance of our approach has been assessed over a number of synthetic and real fuzzy clustering problems with different objective functions and distance measures, from which it is concluded that the proposed approach shows excellent performance in all cases.
10

Yu, Zhenao, Peng Duan, Leilei Meng, Yuyan Han, and Fan Ye. "Multi-objective path planning for mobile robot with an improved artificial bee colony algorithm." Mathematical Biosciences and Engineering 20, no. 2 (2022): 2501–29. http://dx.doi.org/10.3934/mbe.2023117.

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<abstract><p>Effective path planning (PP) is the basis of autonomous navigation for mobile robots. Since the PP is an NP-hard problem, intelligent optimization algorithms have become a popular option to solve this problem. As a classic evolutionary algorithm, the artificial bee colony (ABC) algorithm has been applied to solve numerous realistic optimization problems. In this study, we propose an improved artificial bee colony algorithm (IMO-ABC) to deal with the multi-objective PP problem for a mobile robot. Path length and path safety were optimized as two objectives. Considering the complexity of the multi-objective PP problem, a well-environment model and a path encoding method are designed to make solutions feasible. In addition, a hybrid initialization strategy is applied to generate efficient feasible solutions. Subsequently, path-shortening and path-crossing operators are developed and embedded in the IMO-ABC algorithm. Meanwhile, a variable neighborhood local search strategy and a global search strategy, which could enhance exploitation and exploration, respectively, are proposed. Finally, representative maps including a real environment map are employed for simulation tests. The effectiveness of the proposed strategies is verified through numerous comparisons and statistical analyses. Simulation results show that the proposed IMO-ABC yields better solutions with respect to hypervolume and set coverage metrics for the later decision-maker.</p></abstract>
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Статті в журналах

Дисертації з теми "Encodings and local search operators":

1

Tsogbetse, Israël. "Etude de codages et voisinages d'un espace de recherche. Application à l'ordonnancement de tâches dans des cas contraints." Electronic Thesis or Diss., Bourgogne Franche-Comté, 2024. http://www.theses.fr/2024UBFCA001.

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Les métaheuristiques sont des méthodes de résolution de problèmes d'optimisation qui se basent majoritairement sur une représentation abstraite des solutions sous forme de codage direct ou indirect. L’amélioration d’une solution ou d’un ensemble de solutions en parallèle se fait via des manipulations de ces codages et des algorithmes d’évaluation de la qualité des solutions associées. Le passage d'une solution à une autre implique l'utilisation d'un ou plusieurs opérateurs qui permettent d'explorer l'espace de recherche. En général, les métaheuristiques exploitent ces opérateurs pour améliorer itérativement les solutions jusqu'à l'atteinte d'un optimum local (ou global). Une pléthore de métaheuristiques a été proposée pour résoudre les problèmes d'optimisation combinatoire dont les problèmes d'ordonnancement de tâches. Elles sont souvent dédiées à des classes spécifiques d'instances. Dans ce contexte, les chercheurs proposent fréquemment des algorithmes qui combinent diverses méthodes, en s’attachant à ajuster au mieux les paramètres des différentes parties de leurs algorithmes, mais les performances obtenues restent souvent comparables et l’efficacité dépendante de la classe d’instances résolues. Bien que les codages de solutions et les opérateurs de voisinage soient reconnus comme étant des composants essentiels au sein des métaheuristiques, ils sont rarement examinés conjointement de manière analytique et scientifique.Cette thèse se propose de caractériser les codages de solutions et les opérateurs de voisinage usuellement utilisés en ordonnancement, et particulièrement pour le problème de type job shop et une de ses variantes, pour lesquels on cherche à minimiser le makespan. L'ambition est d'exploiter les propriétés des espaces de recherche induits par ces codages et opérateurs, dans le but d'améliorer la conception des métaheuristiques. La démarche que nous avons appliquée dans notre étude est structurée en deux volets principaux avec une gradation de la complexité du problème de job shop. Le premier volet porte sur la caractérisation d'espaces de recherche à travers une analyse de paysage de fitness, en utilisant des métriques issues de la littérature. Le second volet concerne l'évaluation des performances de diverses associations de codages et d'opérateurs de voisinage dans l'optique de dégager d'éventuelles corrélations avec les propriétés du paysage, pour l'émission de préconisations pour la conception de métaheuristiques. Cette démarche est utilisée dans un premier temps pour un job shop de base, puis pour une variante plus contrainte du job shop : le job shop flexible avec contraintes de transport. Nos travaux mettent en évidence la difficulté de lier les performances des associations testées avec les métriques usuelles. La comparaison des résultats obtenus pour le problème de base et sa variante plus contrainte nous amènent à émettre des réserves sur une généralisation systématique des caractéristiques des codages et des opérateurs pour cette catégorie de problème d'optimisation
Metaheuristics are optimization problem-solving methods that primarily rely on an abstract representation of solutions in the form of direct or indirect encoding. Improving a solution or a set of solutions in parallel is achieved through manipulations of these encodings and algorithms evaluating the quality of associated solutions. The transition from one solution to another involves the use of one or more operators to explore the search space. Generally, metaheuristics utilize these operators to iteratively enhance solutions until reaching a local (or global) optimum. A plethora of metaheuristics has been proposed to address combinatorial optimization problems, including task scheduling problems. These ones are often dedicated to specific classes of instances. In this context, researchers frequently propose algorithms that combine various methods, striving to optimize parameters across different parts of their algorithms. However, the achieved performance is often comparable, and efficiency depends on the class of instances addressed. While solution encodings and neighborhood operators are recognized as essential components within metaheuristics, they are rarely jointly examined in an analytical and scientific manner.This thesis aims to characterize solution encodings and neighborhood operators commonly used in scheduling, particularly for the job shop problem and for one of its variants, in which the objective is to minimize the makespan. The ambition is to exploit the properties of the search spaces induced by these encodings and operators to enhance the design of metaheuristics. The approach applied in our study is structured into two main parts, with a gradation in the complexity of the job shop problem. The first part focuses on characterizing search spaces through a fitness landscape analysis, using metrics from the literature. The second part involves evaluating the performance of various combinations of encodings and neighborhood operators with the aim of identifying potential correlations with landscape properties. This is done to provide recommendations for the design of metaheuristics. This approach is initially applied to a basic job shop and then to a more constrained variant: the flexible job shop with transportation constraints. Our work highlights the challenge of linking the performance of tested combinations with standard metrics. The comparison of results obtained for the basic problem and its more constrained variant leads us to express reservations about a systematic generalization of encoding and operator characteristics for this category of optimization problems

Частини книг з теми "Encodings and local search operators":

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Kordos, Mirosław, Rafał Kulka, Tomasz Steblik, and Rafał Scherer. "Local Search in Selected Crossover Operators." In Computational Science – ICCS 2022, 369–82. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08757-8_31.

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2

Chen, Yujie, Philip Mourdjis, Fiona Polack, Peter Cowling, and Stephen Remde. "Evaluating Hyperheuristics and Local Search Operators for Periodic Routing Problems." In Evolutionary Computation in Combinatorial Optimization, 104–20. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30698-8_8.

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3

Freisleben, Bernd, and Peter Merz. "New genetic local search operators for the traveling salesman problem." In Parallel Problem Solving from Nature — PPSN IV, 890–99. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/3-540-61723-x_1052.

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4

Aboutaib, Brahim, Sébastien Verel, Cyril Fonlupt, Bilel Derbel, Arnaud Liefooghe, and Belaïd Ahiod. "On Stochastic Fitness Landscapes: Local Optimality and Fitness Landscape Analysis for Stochastic Search Operators." In Parallel Problem Solving from Nature – PPSN XVI, 97–110. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58115-2_7.

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5

Nigam, Vivek, Giselle Reis, Samar Rahmouni, and Harald Ruess. "Proof Search and Certificates for Evidential Transactions." In Automated Deduction – CADE 28, 234–51. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-79876-5_14.

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AbstractAttestation logics have been used for specifying systems with policies involving different principals. Cyberlogic is an attestation logic used for the specification of Evidential Transactions (ETs). In such transactions, evidence has to be provided supporting its validity with respect to given policies. For example, visa applicants may be required to demonstrate that they have sufficient funds to visit a foreign country. Such evidence can be expressed as a Cyberlogic proof, possibly combined with non-logical data (e.g., a digitally signed document). A key issue is how to construct and communicate such evidence/proofs. It turns out that attestation modalities are challenging to use established proof-theoretic methods such as focusing. Our first contribution is the refinement of Cyberlogic proof theory with knowledge operators which can be used to represent knowledge bases local to one or more principals. Our second contribution is the identification of an executable fragment of Cyberlogic, called Cyberlogic programs, enabling the specification of ETs. Our third contribution is a sound and complete proof system for Cyberlogic programs enabling proof search similar to search in logic programming. Our final contribution is a proof certificate format for Cyberlogic programs inspired by Foundational Proof Certificates as a means to communicate evidence and check its validity.
6

Bansal, Jagdish Chand, Prathu Bajpai, Anjali Rawat, and Atulya K. Nagar. "Advancements in the Sine Cosine Algorithm." In Sine Cosine Algorithm for Optimization, 87–103. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9722-8_5.

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AbstractIn the last few decades, the development and advancement of meta-heuristic algorithms have become the focus of the research community as these algorithms face various challenges like, balance between exploration and exploitation, tuning of parameters, getting trapped in local optima, and very slow convergence rate. Sine cosine algorithm (SCA) also faces similar kinds of challenges and sometimes fails to perform effectively in finding the global optimal solution. Sine and cosine are trigonometric operators with a 90$$^\circ $$ phase shift from each other. The range of sine and cosine functions lies in the range $$[-1,1]$$. Sine and cosine functions in the position update equation of SCA help solutions to perform search procedure. However, in some situations, SCA promotes similar solutions in the search space, which results in the loss of diversity in the population, and the search process is susceptible to trapping in the region of local optimum [1]. Motivated by these challenges, SCA has been modified to improve its capability and efficiency in several ways. Several strategies have been employed to alter the basic version of SCA [2], aiming to enhance its effectiveness and optimization capabilities. In this chapter, we will discuss about these modifications and strategies, which have been incorporated into the sine cosine algorithm (SCA) in past few years. Apart from this, we will briefly describe the applications of the modified versions of SCA.
7

Durgut, Rafet, and Mehmet Emin Aydin. "Multi Strategy Search with Crow Search Algorithm." In Optimization Algorithms [Working Title]. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.102862.

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Crow Search Algorithm (CSA) is one of the recently proposed swarm intelligence algorithms developed inspiring of the social behaviour of crow flocks. One of the drawbacks of the original CSA is that it tends to randomly select a neighbour on search strategy due to its low convergence rate, which pushes the search to stick in local optima due to the same search strategy applied across iterations. The multi-strategy search for CSA (CSA-MSS) has been proposed to enrich the search facilities and provide diversity to overcome these drawbacks. The multi-strategy search implies utilising a pool of strategies consists of six different types of search operators. The multi-strategy approach with a selection mechanism has not been proposed for CSA before and implemented first time. The comparative performance analysis for the proposed algorithm has been conducted over solving 24 benchmark problems. The results demonstrate that the proposed approach is outperforming well-known state-of-the-art methods.
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Ould Sidi, Hamed, Rachid Ellaia, Emmanuel Pagnacco, and Ahmed Tchvagha Zeine. "An Immune Multiobjective Optimization with Backtracking Search Algorithm Inspired Recombination." In Search Algorithm - Essence of Optimization. IntechOpen, 2023. http://dx.doi.org/10.5772/intechopen.100306.

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We propose a novel hybrid multiobjective (MO) immune algorithm for tackling continuous MO problems. Similarly to the nondominated neighbor immune algorithm (NNIA), it considers the characteristics of OM problems: based on the fitness values, the best individuals from the test population are selected and recombined to guide the rest of the individuals in the population to the Pareto front. But NNIA uses the simulated binary crossover (SBX), which uses the local search method. In our algorithm, the recombination is essentially inspired by the cross used in the backtracking search algorithm (BSA), but the adaptations are found in the immune algorithm. Thus, three variants are designed in this chapter, resulting in new recombination operators. They are evaluated through 10 benchmark tests. For the most advanced proposed variant, which is designed to have global search ability, results show that an improved convergence and a better diversity of the Pareto front are statistically achieved when compared with a basic immune algorithm having no recombination or to NNIA. Finally, the proposed new algorithm is demonstrated to be successful in approximating the Pareto front of the complex 10 bar truss structure MO problem.

Тези доповідей конференцій з теми "Encodings and local search operators":

1

Yin, Shuo, and Guoqiang Zhong. "LGI-GT: Graph Transformers with Local and Global Operators Interleaving." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/501.

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Since Transformers can alleviate some critical and fundamental problems of graph neural networks (GNNs), such as over-smoothing, over-squashing and limited expressiveness, they have been successfully applied to graph representation learning and achieved impressive results. However, although there are many works dedicated to make graph Transformers (GTs) aware of the structure and edge information by specifically tailored attention forms or graph-related positional and structural encodings, few works address the problem of how to construct high-performing GTs with modules of GNNs and Transformers. In this paper, we propose a novel graph Transformer with local and global operators interleaving (LGI-GT), in which we further design a new method propagating embeddings of the [CLS] token for global information representation. Additionally, we propose an effective message passing module called edge enhanced local attention (EELA), which makes LGI-GT a full-attention GT. Extensive experiments demonstrate that LGI-GT performs consistently better than previous state-of-the-art GNNs and GTs, while ablation studies show the effectiveness of the proposed LGI scheme and EELA. The source code of LGI-GT is available at https://github.com/shuoyinn/LGI-GT.
2

Tanigaki, Yuki, Hiroyuki Masuda, Yu Setoguchi, Yusuke Nojima, and Hisao Ishibuchi. "Algorithm structure optimization by choosing operators in multiobjective genetic local search." In 2015 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2015. http://dx.doi.org/10.1109/cec.2015.7256980.

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Green, Jeremy R., Anthony Francis, Parikshit Junnarkar, Miao Chuan, Thomas Rae, and Hartmut Wittig. "Search for a bound H-dibaryon using local six-quark interpolating operators." In The 32nd International Symposium on Lattice Field Theory. Trieste, Italy: Sissa Medialab, 2015. http://dx.doi.org/10.22323/1.214.0107.

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Pourhassan, Mojgan, and Frank Neumann. "On the Impact of Local Search Operators and Variable Neighbourhood Search for the Generalized Travelling Salesperson Problem." In GECCO '15: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2739480.2754656.

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Domınguez-Isidro, Saul, Efren Mezura-Montes, and Guillermo Leguizamon. "Performance comparison of local search operators in differential evolution for constrained numerical optimization problems." In 2014 IEEE Symposium on Differential Evolution (SDE). IEEE, 2014. http://dx.doi.org/10.1109/sde.2014.7031530.

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Gao, Kaizhou, Yicheng Zhang, Yi Zhang, and Rong Su. "A meta-heuristic with ensemble of local search operators for urban traffic light optimization." In 2017 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2017. http://dx.doi.org/10.1109/ssci.2017.8285317.

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Liu, Xin-Xin, Dong Liu, Qiang Yang, Xiao-Fang Liu, Wei-Jie Yu, and Jun Zhang. "Comparative Analysis of Five Local Search Operators on Visiting Constrained Multiple Traveling Salesmen Problem." In 2021 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2021. http://dx.doi.org/10.1109/ssci50451.2021.9659963.

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Rodrigues da Cruz, André, Rodrigo Tomás Nogueira Cardoso, Elizabeth Fialho Wanner, and Ricardo Takahashi. "Local Search Operators Based on Linear-Quadratic Approximations for Multiobjective Problems on a Budget Scenario." In ANAIS DO SIMPóSIO BRASILEIRO DE PESQUISA OPERACIONAL. Rio de Janeiro - Rio de Janeiro, Brasil: Galoa, 2018. http://dx.doi.org/10.59254/sbpo-2018-85409.

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Dominguez-Isidro, Saul, and Efren Mezura-Montes. "Study of direct local search operators influence in memetic differential evolution for constrained numerical optimization problems." In 2017 International Conference on Electronics, Communications and Computers (CONIELECOMP). IEEE, 2017. http://dx.doi.org/10.1109/conielecomp.2017.7891831.

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Katayama, Kengo, Yuto Akagi, Elis Kulla, Hideo Minamihara, and Noritaka Nishihara. "New Kick Operators in Iterated Local Search Based Metaheuristic for Solving the Node Placement Problem in Multihop Networks." In 2014 17th International Conference on Network-Based Information Systems (NBiS). IEEE, 2014. http://dx.doi.org/10.1109/nbis.2014.35.

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