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cultural algorithms, an alternative heuristic to solve the job shop scheduling problem

ENGINEERING OPTIMIZATION 2007年第1期39卷 69-85页

作者： Rivera, Daniel Cortes Becerra, Ricardo Landa Coello, Carlos A. Coello CINVESTAV IPN Evolutionary Computat Grp Dept Ingn ElectrSecc Computac Mexico City 07300 DF Mexico

In this work, an approach for solving the job shop scheduling problem using a cultural algorithm is proposed. cultural algorithms are evolutionary computation methods that extract domain knowledge during the evolutionary process. Additional to this extracted knowledge, the proposed approach also uses domain knowledge given a priori (based on specific domain knowledge available for the job shop scheduling problem). The proposed approach is compared with respect to a Greedy Randomized Adaptive Search Procedure (GRASP), a Parallel GRASP, a Genetic Algorithm, a Hybrid Genetic Algorithm, and a deterministic method called shifting bottleneck. The cultural algorithm proposed in this article is able to produce competitive results with respect to the two approaches previously indicated at a significantly lower computational cost than at least one of them and without using any sort of parallel processing.

关键词： cultural algorithms evolutionary algorithms evolutionary programming job shop scheduling metaheuristics

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cultural algorithms: a Tabu search approach for the optimization of engineering design problems

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SOFT COMPUTING 2014年第8期18卷 1631-1644页

作者： Ali, Mostafa Z. Reynolds, Robert G. Jordan Univ Sci & Technol Ar Ramtha Jordan Wayne State Univ Detroit MI USA

cultural algorithms and Tabu search algorithms are both powerful tools to solve intricate constrained engineering and large-scale multi-modal optimization problems. In this paper, we introduce a hybrid approach that combines cultural algorithms and Tabu search (CA-TS). Here, Tabu Search is used to transform History Knowledge in the Belief Space from a passive knowledge source to an active one. In each generation of the cultural Algorithm, we calculate the best individual solution and then seek the best new neighbor of that solution in the social network for that population using Tabu search. In order to speed up the convergence process through knowledge dissemination, simple forms of social network topologies were used to describe the connectivity of individual solutions. This can reduce the number of needed generations while maintaining accuracy and increasing the search radius when needed. The integration of the Tabu search algorithm as a local enhancement process enables CA-TS to leap over false peaks and local optima. The proposed hybrid algorithm is applied to a set of complex non-linear constrained engineering optimization design problems. Furthermore, computational results are discussed to show that the algorithm can produce results that are comparable or superior to those of other well-known optimization algorithms from the literature, and can improve the performance and the speed of convergence with a reduced communication cost.

关键词： Knowledge diffusion Constrained engineering optimization design Knowledge integration cultural algorithms Tabu search

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cultural algorithms as a Framework for the Design of Trustable Evolutionary algorithms

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INTERNATIONAL JOURNAL OF SEMANTIC COMPUTING 2022年第1期16卷 107-134页

作者： Al-Tirawi, Anas Reynolds, Robert G. Wayne State Univ Dept Comp Sci Detroit MI 48201 USA Univ Michigan Museum Anthropol Archaeol Ann Arbor MI 48201 USA

One of the major challenges facing Artificial Intelligence in the future is the design of trustworthy algorithms. The development of trustworthy algorithms will be a key challenge in Artificial Intelligence for years to come. cultural algorithms (CAs) are viewed as one framework that can be employed to produce a trustable evolutionary algorithm. They contain features to support both sustainable and explainable computation that satisfy requirements for trustworthy algorithms proposed by Cox [Nine experts on the single biggest obstacle facing AI and algorithms in the next five years, Emerging Tech Brew, January 22, 2021]. Here, two different configurations of CAs are described and compared in terms of their ability to support sustainable solutions over the complete range of dynamic environments, from static to linear to nonlinear and finally chaotic. The Wisdom of the Crowds method was selected for the one configuration since it has been observed to work in both simple and complex environments and requires little long-term memory. The Common Value Auction (CVA) configuration was selected to represent those mechanisms that were more data centric and required more long-term memory content. Both approaches were found to provide sustainable performance across all the dynamic environments tested from static to chaotic. Based upon the information collected in the Belief Space, they produced this behavior in different ways. First, the topologies that they employed differed in terms of the "in degree" for different complexities. The CVA approach tended to favor reduced "indegree/outdegree", while the WM exhibited a higher indegree/outdegree in the best topology for a given environment. These differences reflected the fact the CVA had more information available for the agents about the network in the Belief Space, whereas the agents in the WM had access to less available knowledge. It therefore needed to spread the knowledge that it currently had more widely throughout the pop

关键词： Trustability cultural algorithms common value auctions wisdom of the crowds evolutionary algorithms social networks sustainable learning resilience robustness dynamic environments

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cultural algorithms-Based Learning Model for Multi-Agent Systems

Cultural Algorithms-Based Learning Model for Multi-Agent Sys...

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39th Latin American Computing Conference (CLEI)

作者： Teran, Juan Aguilar, Jose Cerrada, Mariela Univ Los Andes CEMISID Merida 5101 Venezuela

ISBN: (纸本)9781479913404;9781479929573

This paper aims to evaluate the learning model for coordination schemes in multiagent systems (MAS) based on cultural algorithms. The model is applied to a case of study in industrial automation, related to the Agents-based System for Fault Management System. The instantiation occurs on the conversations that are defining in the MAS's coordination model, which are characterized by type of conversation that have been previously defined. A conversation can have sub-conversations, and in this case the sub-conversations are characterized by a particular type of conversation. Additionally in these conversations can occur some type of conflict, that can be solved by using different coordination mechanisms existing in the literature. For this, it is developed a model based on cultural algorithms, which is used by the MAS as a learning way in the process to determine which coordination mechanism is more suitable for a given conversation and a given scenario. The results show that the obtained model through this learning guides the MAS to determine which mechanism is better suited for a given conversation.

关键词： cultural algorithms coordination collective learning multi-agent systems

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cultural algorithms: Less is More

Cultural Algorithms: Less is More

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13th International Conference of the Catalan-Association-for-Artificial-Intelligence (CCIA)

作者： Almirall, Esteve Cortes, Ulises ESADE Av Pedralbes 60-62 Barcelona 08034 Spain Technol Univ Catalonia UPC Barcelona Spain

ISBN: (纸本)9781607506430;9781607506423

cultural algorithms, by imitating the social behavior of human communities are able to attain good results while exhibiting a remarkable frugality in resources. This paper explores their needs in terms of sample size and how these needs are affected by the complexity of the information being addressed, with a somewhat surprising result: less is more, smaller sample sizes conduce to better results at any level of complexity.

关键词： Agents Complexity cultural algorithms

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A novel hybrid cultural algorithms framework with trajectory-based search for global numerical optimization

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INFORMATION SCIENCES 2016年 334卷 219-249页

作者： Ali, Mostafa Z. Awad, Noor H. Suganthan, Ponnuthurai N. Duwairi, Rehab M. Reynolds, Robert G. Jordan Univ Sci & Technol Irbid 22110 Jordan Princess Sumayya Univ Technol Amman Jordan Nanyang Technol Univ Singapore 639798 Singapore Wayne State Univ Detroit MI 48202 USA Univ Michigan Ann Arbor MI 48109 USA

In recent years, cultural algorithms (CAs) have attracted substantial research interest. When applied to highly multimodal and high dimensional problems, cultural algorithms suffer from fast convergence followed by stagnation. This research proposes a novel hybridization between cultural algorithms and a modified multiple trajectory search (MTS). In this hybridization, a modified version of cultural algorithms is applied to generate solutions using three knowledge sources namely situational knowledge, normative knowledge, and topographic knowledge. From these solutions, several are selected to be used by the modified multi-trajectory search. All solutions generated by both component algorithms are used to update the three knowledge sources in the belief space of cultural algorithms. In addition, an adaptive quality function is used to control the number of function evaluations assigned to each component algorithm according to their success rates in the recent past iterations. The function evaluations assigned to cultural algorithms are also divided among the three knowledge sources according to their success rates in recent generations of the search. Moreover, the quality function is used to tune the number of offspring these component algorithms are allowed to contribute during the search. The proposed hybridization between cultural algorithms and the modified trajectory-based search is employed to solve a test suite of 25 large-scale benchmark functions. The paper also investigates the application of the new algorithm to a set of real-life problems. Comparative studies show that the proposed algorithm can have superior performance on more complex higher dimensional multimodal optimization problems when compared with several other hybrid and single population optimizers. (C) 2015 Elsevier Inc. All rights reserved.

关键词： cultural algorithms Global numerical optimization Hybrid algorithm Knowledge source Multiple trajectory search

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Integration in industrial automation based on multi-agent systems using cultural algorithms for optimizing the coordination mechanisms

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COMPUTERS IN INDUSTRY 2017年 91卷 11-23页

作者： Teran, Juan Aguilar, Jose Cerrada, Mariela Univ Los Andes CEMISID Dpto Computac Merida Venezuela Univ Los Andes CEMISID Merida Venezuela Escuela Politec Nacl Quito Ecuador Univ Tecn Particular Loja Loja Ecuador Univ Politecn Salesiana GIDTEC Carrera Ingn Mecan Cuenca Ecuador

Integration in industrial automation can be approached from the theory of Distributed Artificial Intelligence. One approach is the modeling of different production units by agents that interact through interaction protocols, which are implemented following a coordination mechanism. Under this approach, integration in automation can be achieved through the optimization of implicit interactions in such mechanisms. This paper presents a strategy for integrating industrial processes based on Multi-Agent Systems (MAS), which consists of optimizing coordination mechanisms that implement conversations between agents, by using cultural algorithms. The cultural algorithm uses formal models of interaction protocols between agents, such as auction and tender, and the integration scheme comes from automation architectures based on MAS, to which their interactions are optimized. The proposed scheme enables data and service-oriented integration. The proposed strategy is applied in two industrial case studies related to the oil production process. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Integration Industrial automation Multi-agent systems cultural algorithms

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Knowledge-based function optimization using fuzzy cultural algorithms with evolutionary programming

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS 2001年第1期31卷 1-18页

作者： Reynolds, RG Zhu, SN Wayne State Univ Dept Comp Sci Detroit MI 48202 USA DaimlerChrysler Dept Informat Technol Auburn Hills MI 48326 USA

In this paper, the advantages of a fuzzy representation in problem solving and search is investigated using the framework of cultural algorithms (CAs), Since all natural languages contain a fuzzy component, the natural question is "Does this fuzzy representation facilitate the problem-solving process within these systems?" In order to investigate this question we use the CA framework of Reynolds [1], CAs are a computational model of cultural evolution derived from and used to express basic anthropological models of culture and its development. A mathematical model of a full fuzzy CA is developed here, In it, the problem solving knowledge is represented using a fuzzy framework. Several theoretical results concerning its properties are presented. The model is then applied to the solution of a set of 12 difficult, benchmark problems in nonlinear real-valued function optimization. The performance of the full fuzzy model is compared with 8 other fuzzy and crisp architectures. The results suggest that a fuzzy approach can produce a statistically significant improvement in search efficiency over nonfuzzy versions for the entire set of functions, We then investigate the class of performance functions for which the full fuzzy system exhibits the greatest improvements over nonfuzzy systems. In general, these are functions which require some preliminary investigation in order to embark on an effective search.

关键词： cultural algorithms evolutionary programming fuzzy set theory nonlinear function optimization

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Multi-machine power system stabilizer design by using cultural algorithms

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2013年第1期44卷 571-580页

作者： Khodabakhshian, Amin Hemmati, Reza Univ Isfahan Dept Elect Engn Esfahan Iran

Conventional power system stabilizer (CPSS) has been widely used as a supplementary controller to damp out the low frequency oscillations. The tuning of CPSS parameters for nonlinear power systems in a robust way in order that the overall system stability can be improved is a major drawback of CPSS. Several meta-heuristic optimization techniques have been tried out to overcome this shortcoming. This paper presents a new technique named cultural algorithms (CAs) to tune the PSS parameters. This technique is robust and computationally efficient as compared with other meta-heuristic algorithms. Simulations are carried out on two typical multi-machine electric power systems. The results clearly verify that the proposed method improves the dynamic stability of the system, especially when the operating point changes. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Power system stabilizer Low frequency oscillations cultural algorithms Genetic algorithms

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Using cultural algorithms to re-engineer large-scale semantic networks

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INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING 2005年第4期15卷 665-693页

作者： Rychtyckyj, N Reynolds, RG Ford Motor Co Mfg Engn Syst Dearborn MI 48121 USA Wayne State Univ Dept Comp Sci Detroit MI 48202 USA

Evolutionary computation has been successfully applied in a variety of problem domains and applications. In this paper we discuss the use of a specific form of evolutionary computation known as cultural algorithms to improve the efficiency of the subsumption algorithm in semantic networks. We identify two complementary methods of using cultural algorithms to solve the problem of re-engineering large-scale dynamic semantic networks in order to optimize the efficiency of subsumption: top-down and bottom-up. The top-down re-engineering approach improves subsumption efficiency by reducing the number of attributes that need to be compared for every node without impacting the results. We demonstrate that a cultural Algorithm approach can be used to identify these defining attributes that are most significant for node retrieval. These results are then utilized within an existing vehicle assembly process planning application that utilizes a semantic network based knowledge base to improve the performance and reduce complexity of the network. It is shown that the results obtained by cultural algorithms are at least as good, and in most cases better, than those obtained by the human developers. The advantage of cultural algorithms is especially pronounced for those classes in the network that are more complex. The goal of bottom-up approach is to classify the input concepts into new clusters that are most efficient for subsumption and classification. While the resultant subsumption efficiency for the bottom-up approach exceeds that for the top-down approach, it does so by removing structural relationships that made the network understandable to human observers. Like a Rete network in expert systems, it is a compilation of only those relationships that impact subsumption. A direct comparison of the two approaches shows that bottom-up semantic network re-engineering creates a semantic network that is approximately 5 times more efficient than the top-down approach in terms of th

关键词： evolutionary computation cultural algorithms semantic networks knowledge-base re-engineering knowledge maintenance subsumption top-down re-engineering

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