The Experts below are selected from a list of 53211 Experts worldwide ranked by ideXlab platform
Rafiqul Islam - One of the best experts on this subject based on the ideXlab platform.
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Multiobjective Computational RNA Design using Chemical Reaction Optimization
2020 4th International Conference on Computer Communication and Signal Processing (ICCCSP), 2020Co-Authors: Mahfujur Rahman Afnan, Naeema Binthe Ashraf, Rafiqul IslamAbstract:RNA Design Problem is an Optimization problem, where a stable primary structure or nucleotide sequence is obtained from a given target RNA secondary structure and is referred as NP-hard. A multiobjective approach based on metaheuristic algorithm named Chemical Reaction Optimization (CRO) combined with a non-dominated sorting approach is used in this paper. In the multiobjective aspect considering three objective functions and a constraint between target and predicted RNA sequences, we used non-dominated sorting to minimize the objective functions of a particular RNA sequence. To improve efficiency, we designed an additional operator named Repair Function to inspect and eliminate the invalid RNA sequence from the solution space. Using 29 structures of RfamDataset, the results of our proposed method named MCROiRNA for computational RNA design problem are compared with other states of the art algorithms to demonstrate that, the performance of our proposed method is satisfactory and it gives more stable sequences with less execution time.
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Dynamic Facility Layout Problem Using Chemical Reaction Optimization
2020 4th International Conference on Computer Communication and Signal Processing (ICCCSP), 2020Co-Authors: Rakibul Hasan Molla, Moushan Naznin, Rafiqul IslamAbstract:In this paper, a renowned meta-heuristic algorithm named chemical Reaction Optimization (CRO) is applied to solve the Dynamic Facility Layout Problem (DFLP). DFLP is an NP-hard problem. This work employed chemical Reaction Optimization to optimize the total manufacturing cost in economic sight. Chemical Reaction Optimization is a population based meta-heuristic algorithm. CRO is applied to DFLP by redesigning its basic operators and designing two new additional operators to get optimal results. The two additional operators are selection and repair operators. The proposed algorithm based on CRO is tested on a benchmark dataset and compared with other algorithms. The experimental results show that our proposed method gives better results than other algorithms in terms of minimization of cost.
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Chemical Reaction Optimization for Solving Resource Constrained Project Scheduling Problem
Cyber Security and Computer Science, 2020Co-Authors: Ohiduzzaman Shuvo, Rafiqul IslamAbstract:In this paper, a renowned metaheuristic algorithm named chemical Reaction Optimization (CRO) is applied to solve the resource constrained project scheduling problem (RCPSP). This work employed chemical Reaction Optimization to schedule project tasks to minimize makespan concerning resource and precedence constraints. Chemical Reaction Optimization is a population-based metaheuristic algorithm. CRO is applied to RCPSP by redesigning its basic operators and taking solutions from the search space using priority-based selection to achieve a better result. The proposed algorithm based on CRO is then tested on large benchmark instances and compared with other metaheuristic algorithms. The experimental results have shown that our proposed method provides better results than other states of art algorithms in terms of both the qualities of result and execution time.
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IJCCI - Chemical Reaction Optimization for Mobile Robot Path Planning.
Proceedings of International Joint Conference on Computational Intelligence, 2020Co-Authors: Pranta Protik, Sudipto Das, Rafiqul IslamAbstract:The goal of mobile robot path planning (MRPP) is to generate a collision-free path while considering certain objective functions, such as path length and smoothness. Nowadays, the application areas of mobile robots have widened significantly. So, it is essential to develop algorithms that will allow them to maneuver effectively in complex environments with obstacles. In this paper, we have proposed a new methodology based on a metaheuristic approach named chemical Reaction Optimization (CRO) to solve this NP-hard problem. We have reconstructed the four fundamental operators and designed two new repair operators to obtain better performance. The results of our proposed algorithm are compared with ant colony Optimization algorithm (ACO), particle swarm Optimization (PSO) algorithm, and genetic algorithm (GA) to show its efficiency in terms of solution quality and computational time.
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Chemical Reaction Optimization: survey on variants
Evolutionary Intelligence, 2019Co-Authors: Rafiqul Islam, C. M. Khaled Saifullah, Riaz MahmudAbstract:Chemical Reaction Optimization (CRO) is a recently established population based metaheuristic for Optimization problems inspired by the natural behavior of chemical Reactions . Optimization is a way of ensuring the usability of resources and related technologies in the best possible way. We experience Optimization problems in our daily lives while some problems are so hard that we can, at best, approximate the best solutions with heuristic or metaheuristic methods. This search (CRO) algorithm inherits several features from other metaheuristics like Simulated Annealing and Genetic Algorithm. After its invention, it was successfully applied to various Optimization problems that were solved by other metaheuristic algorithms . The robustness of CRO algorithm was proved when the comparisons with other evolutionary algorithms like Particle Swarm Optimization, Genetic Algorithm, Simulated Annealing, Ant Colony Optimization, Tabu Search, Bee Colony Optimization etc. showed the superior results. As a result, the CRO algorithm has been started to use for solving problems in different fields of Optimization . In this paper, we have reviewed the CRO based algorithms with respect to some well-known Optimization problems. A brief description of variants of CRO algorithm will help the readers to understand the diversified quality of CRO algorithm. For different problems where CRO algorithms were used, the study on parameters and the experimental results are included to show the robustness of CRO algorithm.
Albert Y. S. Lam - One of the best experts on this subject based on the ideXlab platform.
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Adaptive Chemical Reaction Optimization for Global Numerical Optimization
arXiv: Neural and Evolutionary Computing, 2015Co-Authors: Albert Y. S. LamAbstract:A newly proposed chemical-Reaction-inspired metaheurisic, Chemical Reaction Optimization (CRO), has been applied to many Optimization problems in both discrete and continuous domains. To alleviate the effort in tuning parameters, this paper reduces the number of Optimization parameters in canonical CRO and develops an adaptive scheme to evolve them. Our proposed Adaptive CRO (ACRO) adapts better to different Optimization problems. We perform simulations with ACRO on a widely-used benchmark of continuous problems. The simulation results show that ACRO has superior performance over canonical CRO.
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real coded chemical Reaction Optimization with different perturbation functions
arXiv: Neural and Evolutionary Computing, 2015Co-Authors: Albert Y. S. LamAbstract:Chemical Reaction Optimization (CRO) is a powerful metaheuristic which mimics the interactions of molecules in chemical Reactions to search for the global optimum. The perturbation function greatly influences the performance of CRO on solving different continuous problems. In this paper, we study four different probability distributions, namely, the Gaussian distribution, the Cauchy distribution, the exponential distribution, and a modified Rayleigh distribution, for the perturbation function of CRO. Different distributions have different impacts on the solutions. The distributions are tested by a set of well-known benchmark functions and simulation results show that problems with different characteristics have different preference on the distribution function. Our study gives guidelines to design CRO for different types of Optimization problems.
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an inter molecular adaptive collision scheme for chemical Reaction Optimization
arXiv: Neural and Evolutionary Computing, 2015Co-Authors: Albert Y. S. LamAbstract:Optimization techniques are frequently applied in science and engineering research and development. Evolutionary algorithms, as a kind of general-purpose metaheuristic, have been shown to be very effective in solving a wide range of Optimization problems. A recently proposed chemical-Reaction-inspired metaheuristic, Chemical Reaction Optimization (CRO), has been applied to solve many global Optimization problems. However, the functionality of the inter-molecular ineffective collision operator in the canonical CRO design overlaps that of the on-wall ineffective collision operator, which can potential impair the overall performance. In this paper we propose a new inter-molecular ineffective collision operator for CRO for global Optimization. To fully utilize our newly proposed operator, we also design a scheme to adapt the algorithm to Optimization problems with different search space characteristics. We analyze the performance of our proposed algorithm with a number of widely used benchmark functions. The simulation results indicate that the new algorithm has superior performance over the canonical CRO.
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CEC - Adaptive Chemical Reaction Optimization for global numerical Optimization
2015 IEEE Congress on Evolutionary Computation (CEC), 2015Co-Authors: Albert Y. S. LamAbstract:A newly proposed chemical-Reaction-inspired metaheurisic, Chemical Reaction Optimization (CRO), has been applied to many Optimization problems in both discrete and continuous domains. To alleviate the effort in tuning parameters, this paper reduces the number of Optimization parameters in canonical CRO and develops an adaptive scheme to evolve them. Our proposed Adaptive CRO (ACRO) adapts better to different Optimization problems. We perform simulations with ACRO on a widely-used benchmark of continuous problems. The simulation results show that ACRO has superior performance over canonical CRO.
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Chemical Reaction Optimization for the Set Covering Problem
2014 IEEE Congress on Evolutionary Computation (CEC), 2014Co-Authors: Albert Y. S. LamAbstract:The set covering problem (SCP) is one of the representative combinatorial Optimization problems, having many practical applications. This paper investigates the development of an algorithm to solve SCP by employing chemical Reaction Optimization (CRO), a general-purpose metaheuristic. It is tested on a wide range of benchmark instances of SCP. The simulation results indicate that this algorithm gives outstanding performance compared with other heuristics and metaheuristics in solving SCP.
Tung Khac Truong - One of the best experts on this subject based on the ideXlab platform.
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A hybrid algorithm based on particle swarm and chemical Reaction Optimization for multi-object problems
Applied Soft Computing, 2015Co-Authors: Tien Trong Nguyen, Shaomiao Chen, Tung Khac TruongAbstract:Graphical abstractDisplay Omitted HighlightsA new hybrid method is proposed for multi-object Optimization.The modified chemical Reaction Optimization (CRO) operators are proposed for multi-object Optimization.A new parameter is proposed to balance between CRO operators and particle swarm Optimization operator.Based on crowding distance mechanism, a new method is proposed to increase the diversity of archiving solutions. Over the past decade, the particle swarm Optimization (PSO) has been an effective algorithm for solving single and multi-object Optimization problems. Recently, the chemical Reaction Optimization (CRO) algorithm is emerging as a new algorithm used to efficiently solve single-object Optimization.In this paper, we present HP-CRO (hybrid of PSO and CRO) a new hybrid algorithm for multi-object Optimization. This algorithm has features of CRO and PSO, HP-CRO creates new molecules (particles) not only used by CRO operations as found in CRO algorithm but also by mechanisms of PSO. The balancing of CRO and PSO operators shows that the method can be used to avoid premature convergence and explore more in the search space.This paper proposes a model with modified CRO operators and also adding new saving molecules into the external population to increase the diversity. The experimental results of the HP-CRO algorithm compared to some meta-heuristics algorithms such as FMOPSO, MOPSO, NSGAII and SPEA2 show that there is improved efficiency of the HP-CRO algorithm for solving multi-object Optimization problems.
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Solving 0−1 knapsack problem by artificial chemical Reaction Optimization algorithm with a greedy strategy
Journal of Intelligent & Fuzzy Systems, 2015Co-Authors: Tung Khac Truong, Aijia Ouyang, Tien Trong NguyenAbstract:This paper proposes a new artificial chemical Reaction Optimization algorithm with a greedy strategy to solve 0-1 knapsack problem. The artificial chemical Reaction Optimization (ACROA) inspiring the chemical Reaction process is used to implement the local and global search. A new repair operator integrating a greedy strategy and random selection is used to repair the infeasible solutions. The experimental results have proven the superior performance of ACROA compared to genetic algorithm, and quantum-inspired evolutionary algorithm.
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Roller Bearing Fault Diagnosis Method Based on Chemical Reaction Optimization and Support Vector Machine
Journal of Computing in Civil Engineering, 2015Co-Authors: Junsheng Cheng, Jinde Zheng, Tung Khac TruongAbstract:AbstractSupport vector machine (SVM) parameter Optimization has always been a demanding task in machine learning. The chemical Reaction Optimization (CRO) method is an established metaheuristic for the Optimization problem and is adapted to optimize the SVM parameters. In this paper, a SVM parameter Optimization method based on CRO (CRO-SVM) is proposed. The CRO-SVM classifier is applied to some real-world benchmark data sets, and promising results are obtained. Furthermore, the CRO-SVM is applied to diagnose the roller bearing fault by combining with the local characteristic–scale decomposition (LCD) method. The experimental results show that the combination of CRO-SVM classifiers and the LCD method obtains higher classification accuracy and lower cost time compared to the other methods.
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A hybrid algorithm based on particle swarm and chemical Reaction Optimization
Expert Systems with Applications, 2014Co-Authors: Tien Trong Nguyen, Shiwen Zhang, Tung Khac TruongAbstract:In this paper, a hybrid method for Optimization is proposed, which combines the two local search operators in chemical Reaction Optimization with global search ability of for global optimum. This hybrid technique incorporates concepts from chemical Reaction Optimization and particle swarm Optimization, it creates new molecules (particles) either operations as found in chemical Reaction Optimization or mechanisms of particle swarm Optimization. Moreover, some technical bound constraint handling has combined when the particle update in particle swarm Optimization. The effects of model parameters like InterRate, @c, Inertia weight and others parameters on performance are investigated in this paper. The experimental results tested on a set of twenty-three benchmark functions show that a hybrid algorithm based on particle swarm and chemical Reaction Optimization can outperform chemical Reaction Optimization algorithm in most of the experiments. Experimental results also indicate average improvement and deviate over chemical Reaction Optimization in the most of experiments.
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A Parallel Chemical Reaction Optimization for Multiple Choice Knapsack Problem
Communications in Computer and Information Science, 2014Co-Authors: Tung Khac Truong, Ahmad Salah, Shuangnan FanAbstract:This research proposed a new parallel algorithm based on chemical Reaction Optimization for multiple-choice knapsack problem (MCKP). In the proposed algorithm, master-slave parallel architecture is used and four problem-specific chemical Reaction operators are suggested. The experimental results have proven the superior performance of the proposed algorithm compared to the basic chemical Reaction Optimization.
Victor O.k. Li - One of the best experts on this subject based on the ideXlab platform.
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real coded chemical Reaction Optimization
IEEE Transactions on Evolutionary Computation, 2012Co-Authors: Victor O.k. Li, James J Q YuAbstract:Optimization problems can generally be classified as continuous and discrete, based on the nature of the solution space. A recently developed chemical-Reaction-inspired metaheuristic, called chemical Reaction Optimization (CRO), has been shown to perform well in many Optimization problems in the discrete domain. This paper is dedicated to proposing a real-coded version of CRO, namely, RCCRO, to solve continuous Optimization problems. We compare the performance of RCCRO with a large number of Optimization techniques on a large set of standard continuous benchmark functions. We find that RCCRO outperforms all the others on the average. We also propose an adaptive scheme for RCCRO which can improve the performance effectively. This shows that CRO is suitable for solving problems in the continuous domain.
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chemical Reaction Optimization a tutorial
Memetic Computing, 2012Co-Authors: Victor O.k. LiAbstract:Chemical Reaction Optimization (CRO) is a recently established metaheuristics for Optimization, inspired by the nature of chemical Reactions. A chemical Reaction is a natural process of transforming the unstable substances to the stable ones. In microscopic view, a chemical Reaction starts with some unstable molecules with excessive energy. The molecules interact with each other through a sequence of elementary Reactions. At the end, they are converted to those with minimum energy to support their existence. This property is embedded in CRO to solve Optimization problems. CRO can be applied to tackle problems in both the discrete and continuous domains. We have successfully exploited CRO to solve a broad range of engineering problems, including the quadratic assignment problem, neural network training, multimodal continuous problems, etc. The simulation results demonstrate that CRO has superior performance when compared with other existing Optimization algorithms. This tutorial aims to assist the readers in implementing CRO to solve their problems. It also serves as a technical overview of the current development of CRO and provides potential future research directions.
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chemical Reaction Optimization for task scheduling in grid computing
IEEE Transactions on Parallel and Distributed Systems, 2011Co-Authors: Jin Xu, Victor O.k. LiAbstract:Grid computing solves high performance and high-throughput computing problems through sharing resources ranging from personal computers to supercomputers distributed around the world. One of the major problems is task scheduling, i.e., allocating tasks to resources. In addition to Makespan and Flowtime, we also take reliability of resources into account, and task scheduling is formulated as an Optimization problem with three objectives. This is an NP-hard problem, and thus, metaheuristic approaches are employed to find the optimal solutions. In this paper, several versions of the Chemical Reaction Optimization (CRO) algorithm are proposed for the grid scheduling problem. CRO is a population-based metaheuristic inspired by the interactions between molecules in a chemical Reaction. We compare these CRO methods with four other acknowledged metaheuristics on a wide range of instances. Simulation results show that the CRO methods generally perform better than existing methods and performance improvement is especially significant in large-scale applications.
Huang Sixu - One of the best experts on this subject based on the ideXlab platform.
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A Transformer Fault Diagnosis Model Based on Chemical Reaction Optimization and Twin Support Vector Machine
Energies, 2019Co-Authors: Yuan Fang, Guo Jiang, Zhihuai Xiao, Zeng Bing, Wenqiang Zhu, Huang SixuAbstract:The condition monitoring and fault diagnosis of power transformers plays a significant role in the safe, stable and reliable operation of the whole power system. Dissolved gas analysis (DGA) methods are widely used for fault diagnosis, however, their accuracy is limited by the selection of DGA features and the performance of fault diagnosis models, for example, the classical support vector machine (SVM), is easily affected by unbalanced training samples. This paper presents a transformer fault diagnosis model based on chemical Reaction Optimization and a twin support vector machine. Twin support vector machines (TWSVMs) are used as classifiers for solving problems involving unbalanced and insufficient samples. Restricted Boltzmann machines (RBMs) are used for data preprocessing to ensure the effective identification of feature parameters and improve the efficiency and accuracy of fault diagnosis. The chemical Reaction Optimization (CRO) algorithm is used to optimize TWSVM parameters to select the optimal training parameters. The cross-validation (CV) method is used to ensure the reliability and generalization ability of the diagnostic model. Finally, the validity of the model is verified using real fault samples and random testing.
