Chemical Reaction Optimization Algorithm Research Articles

RNA Secondary Structure Prediction with Pseudoknots Using Chemical Reaction Optimization Algorithm

RNA molecules play a significant role in cell function especially including pseudoknots. In past decades, several methods have been developed to predict RNA secondary structure with pseudoknots and the most popular one uses minimum free energy. It is a nondeterministic polynomial-time hard (NP-hard) problem. We have proposed an approach based on a metaheuristic algorithm named Chemical Reaction Optimization (CRO) to solve the RNA pseudoknotted structure prediction problem. The reaction operators of CRO algorithm have been redesigned and used on the generated population to find the structure with the minimum free energy. Besides, we have developed an additional operator called Repair operator which has a great influence on our algorithm in increasing accuracy. It helps to increase the true positive base pairs while decreasing the false positive and false negative base pairs. Four energy models have been applied to calculate the energy. To evaluate the performance, we have used four datasets containing RNA pseudoknotted sequences taken from the RNA STRAND and Pseudobase++ database. We have compared the proposed approach with some existing algorithms and shown that our CRO based model is a better prediction method in terms of accuracy and speed.

Fog Computing Vehicular Network Resource Management Based on Chemical Reaction Optimization

The Internet of vehicles (IoV) provides strong support for ensuring the diversification of urban transportation as the core of next generation intelligent transportation. However, with the rapid growth of vehicle numbers and mobile data, traditional IoV fails to meet the real-time and reliable communication requirements of modern intelligent transportation due to its singleness and low flexibility. In this paper, we study the resource management issues in the IoV, which aim to optimize the energy efficiency of the system. Meanwhile, a non-orthogonal multiple access (NOMA)-based fog computing vehicular (FCV) network architecture is proposed. By splitting the resource management problem into two subproblems of subchannel and power allocation, chemical reaction optimization (CRO) algorithm and real-coded chemical reaction optimization (RCCRO) algorithm are utilized to solve subchannel and power allocation problem, respectively. Also fog computing (FC) technology is introduced to improve local storage and computing capabilities in IoV. Simulation results prove the effectiveness of the proposed scheme.

A comparative study of meta-heuristics for local path planning of a mobile robot

ABSTRACT Recent trends in path planning have led to a proliferation of studies that find solutions to the path planning problems in an unknown cluster environment. This study aims to find an optimum impact-free path length for a mobile robot with a multi-objective optimization approach. The multi-objective optimization problem is formulated by using path length and a safety aspect as the two objectives. A hybrid population-based optimization algorithm, i.e. the hybrid particle swarm and chemical reaction optimization (HPCRO) algorithm, has been used to obtain a smooth path for the robot in an unknown environment with circular and/or polygonal obstacles. The results of the HPCRO algorithm are then compared with those of genetic algorithms, chemical reaction optimization and particle swarm optimization. Some statistical tests are performed to illustrate the superiority and potential applicability of the hybrid algorithm. The results of the hybrid algorithm are encouraging in terms of cost function value and computational cost.

A Hybrid Chemical Reaction Optimization Algorithm for Solving 3D Packing Problem

This paper combines the chemical reaction optimisation algorithm with the greedy algorithm to solve the three-dimensional bin packing problem (3D-BPP). Traditionally, the 3D-BPP is solved with algorithms based on particle swarm optimisation, genetic algorithm, or simulated annealing algorithm, etc. The chemical reaction optimisation algorithm is proposed in recent years, which has the advantages of traditional simulated annealing algorithm and has a faster convergence speed. After making appropriate adjustments to the chemical reaction optimisation algorithm, the experiment was performed on classic 320 examples. The results show that the proposed algorithm outperforms the existing algorithms such as greedy variable neighbourhood descent (GVND) and biased random key genetic algorithm (BRKGA).

Modified chemical reaction optimization and its application in engineering problems.

Chemical Reaction Optimization (CRO) is a simple and efficient evolutionary optimization algorithm by simulating chemical reactions. As far as the current research is concerned, the algorithm has been successfully used for solving a number of real-world optimization tasks. In our paper, a new real encoded chemical reaction optimization algorithm is proposed to boost the efficiency of the optimization operations in standard chemical reactions optimization algorithm. Inspired by the evolutionary operation of the differential evolution algorithm, an improved search operation mechanism is proposed based on the underlying operation. It is modeled to further explore the search space of the algorithm under the best individuals. Afterwards, to control the perturbation frequency of the search strategy, the modification rate is increased to balance between the exploration ability and mining ability of the algorithm. Meanwhile, we also propose a new population initialization method that incorporates several models to produce high-quality initialized populations. To validate the effectiveness of the algorithm, nine unconstrained optimization algorithms are used as benchmark functions. As observed from the experimental results, it is evident that the proposed algorithm is significantly better than the standard chemical reaction algorithm and other evolutionary optimization algorithms. Then, we also apply the proposed model to address the synthesis problem of two antenna array synthesis. The results also reveal that the proposed algorithm is superior to other approaches from different perspectives.

Retracted] Study of Node Distribution and Density Optimization in Mobile Sensor Network 3D Space

Mobile sensor network is applied in information collection in emergencies. As the mobile sensor network in real environment is widely deployed with different height and the redundancy of the sensor node needs to be as low as possible, therefore, it is necessary to effectively deploy mobile sensor nodes in the 3D space to have reasonable layout and optimized density. To this end, we established the optimization model of mobile sensor network deployment and solved the model with chemical reaction optimization (CRO). The experimental results have shown that compared with traditional particle swarm optimization (PSO), CRO algorithm can achieve reasonable deployment more rapidly and enhance the network performance evaluation value effectively. The reasonable deployment of mobile sensor network node is very significant to information collecting, postperiod decision‐making, and rapid rescuing work in emergencies.

A hybrid chemical reaction optimisation algorithm for solving 3D packing problem

A hybrid chemical reaction optimisation algorithm for solving 3D packing problem

Techno-economic analysis of hydrogen energy for renewable energy power smoothing

With the increasing proportion of renewable energy (mainly wind power and photovoltaic) connected to the grid, the fluctuation of renewable energy power brings great challenges to the safe and reliable operation of power grid. As a clean, low-carbon secondary energy, hydrogen energy is applied in renewable energy (mainly wind power and photovoltaic) grid-connected power smoothing, which opens up a new way of coupling hydrogen storage energy with renewable energy. This paper focuses on the optimization of capacity of electrolyzers and fuel cells and the analysis of system economy in the process of power output smoothing of wind/photovoltaic coupled hydrogen energy grid-connected system. Based on the complementary characteristics of particle swarm optimization (PSO) and chemical reaction optimization algorithm (CROA), a particle swarm optimization-chemical reaction optimization algorithm (PSO-CROA) are proposed. Aiming at maximizing system profit, the capacity of electrolyzers and fuel cells are constrained by wind power fluctuation, and considering environmental benefits, government subsidies and time value of funds, the objective function and its constraints are established. According to the simulation analysis, by comparing the calculated results with PSO and CROA, it shows that PSO-CROA effectively evaluates the economy of the system, and optimizes the optimal capacity of the electrolyzers and fuel cells. The conclusion of this paper is of great significance for the application of hydrogen energy storage in the evaluation of power smoothness and economy of renewable energy grid connection and the calculation of economic allocation of hydrogen energy storage capacity.

E2MR-HOA: Conservation of Energy through Multi-Hop Routing Protocol for WSN’S Using Hybrid Optimization Algorithm

Consuming energy at the maximal level is a major concern in wireless sensor networks (WSNs). Many researchers focus on reducing and preserving the energy. The duration of active network of WSNs is affected by energy consumption of sensor nodes. For typical applications such as structure monitoring, border surveillance, integrated into the external surface of a pipeline, and clambered along the sustaining structure of a bridge, sensor node energy efficiency is an important issue. The paper proposed an energy-efficient multi-hop routing protocol using hybrid optimization algorithm (E2MR-HOA) for WSNs. The proposed routing protocol consists of two algorithms, i.e., hybrid optimization algorithm. We present modified chemical reaction optimization (MCRO) algorithm to form clusters and select cluster head (CH) among the cluster members. Then the modified bacterial forging search (MBFS) algorithm is used to compute reliable route between source to destination. The proposed E2MR-HOA protocol is evaluated using NS2 simulations. The simulation result shows that the proposed routing protocol provides significant energy efficiency with network lifetime over the existing routing protocols.

An improved chemical reaction optimization algorithm for solving the shortest common supersequence problem

The shortest common supersequence (SCS) problem is a classical NP-hard problem, which is normally solved by heuristic algorithms. One important heuristic that is inspired by the process of chemical reactions in nature is the chemical reaction optimization (CRO) and its algorithm known as CRO_SCS. In this paper we propose a novel CRO algorithm, dubbed IMCRO, to solve the SCS problem efficiently. Two new operators are introduced in two of the four reactions of the CRO: a new circular shift operator is added to the decomposition reaction, and a new two-step crossover operator is included in the inter-molecular ineffective collision reaction. Experimental results show that IMCRO achieves better performance on random and real sequences than well-known heuristic algorithms such as the ant colony optimization, deposition and reduction, enhanced beam search, and CRO_SCS. Additionally, it outperforms its baseline CRO_SCS for DNA instances, averaging a SCS length reduction of 1.02, with a maximum length reduction of up to 2.1.

Optimal reconfiguration of capacitor based radial distribution system using chaotic quasi oppositional chemical reaction optimization

This paper develops an improved version of the chemical reaction optimization (CRO) algorithm based on the opposition-based learning (OBL) strategy named quasi-oppositional CRO (QOCRO) for optimal reconfiguration of a power system to minimize power loss of the network. Furthermore, to avoid suboptimal solutions and to increase the convergence rate, chaotic behavior is mapped with QOCRO, which results in chaotic QOCRO (CQOCRO). The reconfiguration technique can minimize power loss up to a certain level. Further power loss reduction may be accomplished by locating the capacitor in the optimal location. To investigate the performance of the proposed CQOCRO, QOCRO, and CRO approaches, they are successfully implemented on two test systems, namely 33-bus and 69-bus radial distribution systems. Moreover, the numerical results are compared with other population-based optimization techniques like krill herd (KH) algorithm, oppositional krill herd (OKH) algorithm, and fuzzy approach. The computational results reveal that CQOCRO is superior to QOCRO, CRO, and other algorithms available in the literature in this domain. Finally, a convergence graph is given to identify the convergence superiority of CQOCRO.

An Interval Forecasting Model Based on Phase Space Reconstruction and Weighted Least Squares Support Vector Machine for Time Series of Dissolved Gas Content in Transformer Oil

Transformer state forecasting and fault forecasting are important for the stable operation of power equipment and the normal operation of power systems. Forecasting of the dissolved gas content in oil is widely conducted for transformer faults, but its accuracy is affected by data scale and data characteristics. Based on phase space reconstruction (PSR) and weighted least squares support vector machine (WLSSVM), a forecasting model of time series of dissolved gas content in transformer oil is proposed in this paper. The phase spaces of time series of the dissolved gas content sequence are reconstructed by chaos theory, and the delay time and dimension are obtained by the C-C method. The WLSSVM model is used to forecast time series of dissolved gas content, the chemical reaction optimization (CRO) algorithm is used to optimize training parameters, the bootstrap method is used to build forecasting intervals. Finally, the accuracy and generalization ability of the forecasting model are verified by the analysis of actual case and the comparison of different models.

A Decomposition-Based Multiobjective Chemical Reaction Optimization Algorithm for Community Detection in Complex Networks

Community detection structure is very important for understanding the organization of the complex networks. This problem is NP-hard, which is modeled as a seriously nonlinear optimization problem. Recently, different intelligence algorithm has shown promising results for this problem. The chemical reaction optimization (CRO) algorithm is a novel evolutionary algorithm which mimics the phenomenon of interactions among molecules in a container. The one characteristic of CRO is that the size of the population is changing. In this paper, we redefined the operator of CRO, and using the method of multiobjective decomposition decomposed the community detection problem into a scalar of sub-problems and using the proposed a discrete variant of CRO (MODCRO) to optimization. In the proposed method, neighbor-based turbulence of on-wall ineffective collision operator and decomposition operator are redefined which is responsible for searching local exploitation ability of algorithm, and the intermolecular ineffective collisions operator and synthesis operator is also redesigned which is responsible for searching global exploration ability of algorithm. Experimental results clearly demonstrate that the proposed algorithm outperforms a number of state-of-the-art multiobjective optimization evolutionary algorithms (MOEAs) on modularity.

Hybrid Artificial Chemical Reaction Optimization Algorithm for Cluster Analysis

Heuristic algorithms have significant contribution in the clustering field. In present work, a hybrid version of the artificial chemical reaction optimization algorithm (HACRO) is proposed to optimize clustering problems. As exploration and exploitation are two major aspects that require balanced coordination among algorithmic steps. The artificial chemical reaction suffers from slower convergence speed due to its poor exploitation mechanism. Moreover, it requires more execution time. Henceforth, to enhance the convergence speed and to make balance among algorithmic space a hybrid version of ACRO is developed. In present work, the artificial chemical reaction optimization algorithm is incorporated with crossover and mutation operator of genetic algorithm. Further, the efficiency of the HACRO algorithm is examined on seven benchmark datasets and collated with ACO, PSO, K-means, GA, ICSO and ACRO clustering algorithms. The present investigation indicated that the proposed algorithm works efficiently in clustering field.

Optimization of protein folding using chemical reaction optimization in HP cubic lattice model

Protein folding optimization is a very important and tough problem in computational biology. For solving this problem, a population-based metaheuristic algorithm named chemical reaction optimization (CRO) with HP cubic lattice model has been proposed in this paper. The proposed algorithm is combined with evolution and H&P compliance mechanisms which are responsible for increasing the performance of the algorithm. The evolution mechanism improves the performance of each individual solution. On the other hand, the H&P compliance mechanism tries to place the H monomer close to the center and place the P monomer as far as possible from the center of the related structure. The algorithm also applies four reactant operations of typical CRO algorithm decomposition, on-wall ineffective collision, synthesis and inter-molecular ineffective collision to solve the problem efficiently. The reactants or mechanisms may cause overlapping of the corresponding solutions. The algorithm also includes a repair mechanism which transforms invalid solutions into valid ones by removing overlapping in cubic lattice points. This algorithm has been tested over some sets of sequences and it shows very good performance.

An Energy Efficient Algorithm for Workflow Scheduling in IaaS Cloud

Energy efficient workflow scheduling is the demand of the present time’s computing platforms such as an infrastructure-as-a-service (IaaS) cloud. An appreciable amount of energy can be saved if a dynamic voltage scaling (DVS) enabled environment is considered. But it is important to decrease makespan of a schedule as well, so that it may not extend beyond the deadline specified by the cloud user. In this paper, we propose a workflow scheduling algorithm which is inspired from hybrid chemical reaction optimization (HCRO) algorithm. The proposed scheme is shown to be energy efficient. Apart from this, it is also shown to minimize makespan. We refer the proposed approach as energy efficient workflow scheduling (EEWS) algorithm. The EEWS is introduced with a novel measure to determine the amount of energy which can be conserved by considering a DVS-enabled environment. Through simulations on a variety of scientific workflow applications, we demonstrate that the proposed scheme performs better than the existing algorithms such as HCRO and multiple priority queues genetic algorithm (MPQGA) in terms of various performance metrics including makespan and the amount of energy conserved. The significance of the proposed algorithm is also judged through the analysis of variance (ANOVA) test and its subsequent LSD analysis.

True Power Loss Reduction by Chemical Reaction Optimization Algorithm

True Power Loss Reduction by Chemical Reaction Optimization Algorithm

Chemical reaction optimization: survey on variants

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.

A new meta-heuristic algorithm based on chemical reactions for partitional clustering problems

In the field of engineering, heuristic algorithms are widely adopted to solve variety of optimization problems. These algorithms have proven its efficacy over classical algorithms. It is seen that chemical reactions consist of an efficient computational procedure to design a new product. The formation of new product contains numbers of objects, states, events and well defined procedural steps. A meta-heuristic algorithm inspired through chemical reaction is developed, called artificial chemical reaction optimization (ACRO) algorithm. In this work, an ACRO algorithm is adopted to solve partitional clustering problems. But, this algorithm suffers with slow convergence rate and sometimes stuck in local optima. To handle these aforementioned problems, two operators are inculcated in ACRO algorithm. The performance of proposed algorithm is tested over well-known clustering datasets. The simulation results confirm that proposed ACRO algorithm is an effective and competitive algorithm to solve partitional clustering problems.

A Transformer Fault Diagnosis Model Based on Chemical Reaction Optimization and Twin Support Vector Machine

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.