Chemical Reaction Optimization Algorithm Research Articles

A Novel Method for Targeted Identification of Essential Proteins by Integrating Chemical Reaction Optimization and Naive Bayes Model.

Targeted identification of essential proteins is of great significance for species identification, drug manufacturing, and disease treatment. It is a challenge to analyze the binding mechanism between essential proteins and improve the identification speed while ensuring the accuracy of the identification. This paper proposes a novel method called EPCRO for identifying essential proteins, which incorporates the chemical reaction optimization (CRO) algorithm and the naive Bayes model to effectively detect essential proteins. In EPCRO, the naive Bayes model is employed to analyze the homogeneity between proteins. In order to improve the identification rate and speed of essential proteins, the protein homogeneity rate is integrated into the CRO algorithm to balance between local and global searches. EPCRO is experimentally compared with 17 existing methods (including, DC, SC, IC, EC, LAC, NC, PeC, WDC, EPD-RW, RWHN, TEGS, CFMM, BSPM, AFSO-EP, CVIM, RWEP, and EPPSODC) based on biological datasets. The results show that EPCRO is superior to the above methods in identification accuracy and speed.

A Hybrid Metaheuristic Algorithm Using Elitist Chemical Reaction Optimization and Learning from Knowledge Assimilation for Improving Rule-based Classification Models

There are various paradigms of learning. One such paradigm is learning from knowledge assimilation. In machine learning, such a paradigm may be considered where concepts (classification models) generated from different algorithms applied to the same training data are combined to come up with a single integrated improved concept (classification model). This is different from combining outputs of various models as in Bagging or Boosting. Thus, learning from knowledge assimilation creates different alternative models that work on different parts of the search space. Such models can be separately optimized so that the best-performing model can be selected for the given problem. For optimization there are several algorithms and Chemical Reaction Optimization (CRO) is a chemistry-based nature-inspired optimization algorithm. In this algorithm, a set of optimization approaches are introduced corresponding to various types of chemical reactions. Classification models in our approach are optimized using a technique called Elitist CRO(ECRO). ECRO is an improvement over CRO, where the optimization approaches are modified making the convergence of the algorithm better than CRO. Although for classification CRO models based on neural networks exist, however, they suffer from interpretability. This paper proposes an application of a new Hybrid Elitist Chemical Reaction Optimization (HECRO) algorithm to generate interpretable classification rules. The proposed algorithm is an extension of CRO and ECRO, in the manner of region exploration of the search space, which is expanded by combining classification rules generated from various algorithms as the initial population following learning from knowledge assimilation. Experimental results with 20 datasets drawn from the UCI Machine Learning data repository show improvement over several performance measures in comparison to the base classifiers used for population generation in HECRO.

Optimization methods to solve the set covering problems

Phasor Measurement Unit Placement aims to decrease the cost this phasor equipment while maintaining the observability of the electrical distribution systems. This placement can be modeled as a set covering problem. Synchronized phasor data provides electrical systems’ information with higher quality and frequency than Remote Terminal Units. These data enable the implementation of faster than real-time state estimators. Thus, supervisory control and data acquisition systems can be replaced by automatically monitored systems that add reliability, efficiency, and resilience to smart grid systems. Due to the flexibility and large-scale in the distribution systems, heuristic methods have frequently been applied to this placement problem. Since the use of Chemical Reaction Optimization (CRO) algorithm is not recurrent, we have decided to implement it together with a topological system observability analysis. Initial population creation, parameterization, elementary reactions of CRO, and local search strategies are presented in this study. Computational results were performed in IEEE power system test cases. The optimization method proposed has obtained optimal results at a lower computation time than the literature. To test in large-scale, we have applied another optimization method to Set Covering Problems from Beasley library, with different parameters. However, the results were not satisfactory indicating that the CRO might not be suitable to some large-scale instances of the Set Covering Problems this library. In this case, further studies are required.

Node deployment optimization of underwater wireless sensor networks using intelligent optimization algorithm and robot collaboration

This study aims to optimize the node deployment of underwater wireless sensor networks (UWSNs) using intelligent optimization algorithms and robot collaboration technology to enhance network performance and coverage. The study employs the chemical reaction optimization (CRO) algorithm, which combines the advantages of genetic algorithms, simulated annealing algorithms, and ant colony algorithms. The CRO algorithm is enhanced through a structure correction function to determine the optimal node deployment scheme to achieve effective and optimal coverage control of the UWSN. Additionally, the flexibility and autonomy of robots are leveraged to improve the efficiency of node deployment and address the unique challenges posed by the underwater environment. Furthermore, the study conducts a comparative analysis of different intelligent optimization algorithms and demonstrates the effectiveness and advantages of the enhanced CRO algorithm in optimizing node deployment for UWSNs. The study findings reveal that the improved algorithm achieves an average coverage rate of 95.66%, significantly outperforming traditional intelligent optimization algorithms. The coverage of UWSNs can be significantly improved by utilizing the enhanced CRO algorithm and robot collaboration technology for node deployment optimization, which offers an effective approach for achieving optimal node deployment. Moreover, the rational deployment of nodes enhances the monitoring capability, resource utilization efficiency, and accuracy of environmental monitoring in underwater networks. The results of this study hold great practical significance for underwater environment monitoring, marine resource exploration, and marine scientific research.

Secure massive MIMO system with two-way relay cooperative transmission in 6G networks

With the advent of Internet of Everything and the era of big data, massive multiple-input multiple-output (MIMO) is considered as an essential technology to meet the growing communication requirements for beyond 5G and the forthcoming 6G networks. This paper considers a secure massive MIMO system, where the legitimate user and the base station exchange messages via two-way relays with the presence of passive eavesdroppers. To achieve the trade-off between the physical-layer security and communication reliability, we design a cooperative transmission mode based on multiple-relay collaboration, where some relays broadcast the received signals and other relays act as friendly jammers to prevent the interception by eavesdroppers. A quantum chemical reaction optimization (QCRO) algorithm is proposed to find the most suitable scheme for multiple-relay collaboration. Simulation results highlight excellent performance of the proposed transmission mode under QCRO in different communication scenarios, which can be considered as a potential solution for the security issue in future wireless networks.

Emergency task offloading strategy based on cloud-edge-end collaboration for smart factories

The realization of smart factories is becoming increasingly possible due to the rapid development of edge computing technology and 5G communication technology. Smart devices in smart factories can perform tasks such as environmental monitoring, production scheduling, and autonomous mobility. These tasks cannot be completed on time depending only on the computing power of smart devices. Therefore, some tasks need to be performed on edge servers and cloud servers. However, events such as natural disasters, network attacks, and hardware failures will cause server computing resource failures. These threats bring hidden dangers to the normal operation of smart factories. This paper proposes an emergency offloading strategy based on cloud–edge-end collaboration for smart factories. In this strategy, the total task execution delay and the key task execution delay constitute an objective function, which is solved by a Fast Chemical Reaction Optimization (Fast-CRO) algorithm. The algorithm follows the principle of prioritizing the offloading of key tasks in emergency situations, and it quickly makes emergency offloading decisions for the system. Simulation results show that this strategy outperforms the other five baseline algorithms and existing priority based CRO algorithms.

Identification of Essential Protein Using Chemical Reaction Optimization and Machine Learning Technique.

For the survival, development, and reproduction of the organism, understanding the working process of the cell, disease study, design drugs, etc. essential protein plays a crucial role. Due to a large number of biological information, computational methods are becoming popular in recent times to identify the essential protein. Many computational methods used machine learning techniques, metaheuristic algorithms, etc. to solve the problem. The problem with these methods is that the essential protein class prediction rate is still low. Many of these methods have not considered the imbalance characteristics of the dataset. In this paper, we have proposed an approach to identify essential proteins using a metaheuristic algorithm named Chemical Reaction Optimization (CRO) and machine learning method. Both topological and biological features are used here. The Saccharomyces cerevisiae (S. cerevisiae) and Escherichia coli (E. coli) datasets are used in the experiment. Topological features are calculated from the PPI network data. Composite features are calculated from the collected features. Synthetic Minority Over-sampling Technique and Edited Nearest Neighbor (SMOTE+ENN) technique is applied to balance the dataset and then the CRO algorithm is applied to achieve the optimal number of features. Our experiment shows that the proposed approach gives better results in both accuracy and f-measure than the existing related methods.

Intelligent Salp Swarm Scheduler With Fitness Based Quasi-Reflection Method for Scientific Workflows in Hybrid Cloud-Fog Environment

The burgeoning volume of data from the IoT applications and intelligent devices processed on the cloud data centers can lead to network congestion and transmission delay. Compared to cloud computing, fog computing focuses on ubiquitous connected heterogeneous devices and addresses the transmission latency by placing the fog nodes at the network edge. Concerning the limited resources of fog nodes enable the computationally intensive tasks to offload on the cloud resources. Scheduling of deadline-constrained workflows with minimum execution cost is challenging due to complex and uncertain computation offloading problems. Therefore, an intelligent fuzzy scheduler is designed to offload tasks characterized with uncertain parameters to the appropriate resources. A new salp swarm algorithm has been exploited to learn and optimize fuzzy task-resource allocation rules. In addition to this, to overcome the shortcomings of the salp swarm algorithm, it is employed with one of the best opposition methods named: Fitness-based quasi-reflection method. The inclusion of the opposition method enhances the proposed ISSS-FQR (Intelligent salp swarm scheduler with the fitness-based quasi-reflection method) approach and improves the learning process. Simulation studies on the benchmark workflows are carried out to demonstrate the efficacy of ISSS-FQR. ISSS-FQR has been compared with the classical algorithms, including chemical reaction optimization and ant colony optimization algorithms for workflow scheduling problems (CR-AC), Cost-Makespan aware scheduling (Deadline-based CMaS), and Directional and non-local convergent particle swarm optimization (DNCPSO). From the analyzed result, ISSS-FQR outperforms the rest of the classical algorithms, which proves the effectiveness of ISSS-FQR. Note to Practitioners—This paper provides a novel method called ISSS-FQR for minimizing the cost of execution of IoT applications while satisfying the deadline constraint. An intelligent fuzzy scheduler is designed to offload tasks characterized with uncertain parameters to the appropriate resources. The ISSS-FQR combines the Salp Swarm Algorithm and the OBL method named FQR to learn the task-resource allocation rules. It is compared with three state-of-the-art algorithms called CR-AC, Deadline-based CMaS, and DNCPSO. From the analyzed result, it has been observed that ISSS-FQR outperforms the previous algorithms.

A solution method to maximal covering location problem based on chemical reaction optimization (CRO) algorithm

A solution method to maximal covering location problem based on chemical reaction optimization (CRO) algorithm

An Energy-Aware Load Balancing Method for IoT-Based Smart Recycling Machines Using an Artificial Chemical Reaction Optimization Algorithm

Recycling is very important for a sustainable and clean environment. Developed and developing countries are both facing the problem of waste management and recycling issues. On the other hand, the Internet of Things (IoT) is a famous and applicable infrastructure used to provide connection between physical devices. It is an important technology that has been researched and implemented in recent years that promises to positively influence several industries, including recycling and trash management. The impact of the IoT on recycling and waste management is examined using standard operating practices in recycling. Recycling facilities, for instance, can use IoT to manage and keep an eye on the recycling situation in various places while allocating the logistics for transportation and distribution processes to minimize recycling costs and lead times. So, companies can use historical patterns to track usage trends in their service regions, assess their accessibility to gather resources, and arrange their activities accordingly. Additionally, energy is a significant aspect of the IoT since several devices will be linked to the internet, and the devices, sensors, nodes, and objects are all energy-restricted. Because the devices are constrained by their nature, the load-balancing protocol is crucial in an IoT ecosystem. Due to the importance of this issue, this study presents an energy-aware load-balancing method for IoT-based smart recycling machines using an artificial chemical reaction optimization algorithm. The experimental results indicated that the proposed solution could achieve excellent performance. According to the obtained results, the imbalance degree (5.44%), energy consumption (11.38%), and delay time (9.05%) were reduced using the proposed method.

Chemical Reaction Optimization for Minimum Weight Dominating Set

Dominating set of a graph can be defined as the set of vertices that can cover all other vertices of the graph. The minimum weight dominating set (MWDS) is the minimum number of vertices in the dominating set with minimum total weight. In recent times, the chemical reaction optimization algorithm (CRO) has shown its supremacy in solving these types of problems. Therefore in this paper, a novel approach based on CRO has been proposed to solve the MWDS problem. The proposed method uses a repair-based technique to generate a molecule. To make the solution feasible by covering all vertices and to get better results, three supporting operators are implemented along with the CRO operators. Besides this, two repair operators are introduced. In the first repair operator, the searching procedure works based on the scaling properties of vertices, and the second one is a unique method for eliminating common neighbors of vertices of the dominating set. The performance of the proposed method is better than any other existing related algorithms. The performance is measured from different graphs of the benchmark datasets. It can be mentioned that the proposed method takes minimal running time to obtain the minimum weight compared to other benchmark methods.

PIRAP: Chaotic Fuzzy Encryption (CFE) Technique and Greedy Chemical Reaction Optimization (GCRO) Algorithm-Based Secured Mobi-Cloud Framework

This paper uses the chaotic fuzzy encryption (CFE) technique and a greedy chemical reaction optimization algorithm based on mobile cloud computing to protect data from any assault. Mobile users who use protected mobile cloud computing (Mobi-Cloud) must trust the cloud service provider to keep the data sent from their mobile devices safe when mobile users express strong reservations about storing personal information in the public cloud. The Greedy Chemical Reaction Optimization (G-CRO) algorithm reduces critical limitations by determining the offloading end with motion at run time, i.e. the task’s operating time, CPU usage, memory utilization, and energy usage. The experimental results show that the proposed method performs better in terms of file uploading time, file downloading time, memory usage in file uploading, memory usage in file downloading, encryption time, decryption time, memory usage on file encryption, memory usage on file decryption, security levels, and key generation time. Compared to the Proxy Re-Encryption (PRE) algorithm, the suggested method consumes less time for key creation (26.04).

A New Energy-Aware Method for Gas Lift Allocation in IoT-Based Industries Using a Chemical Reaction-Based Optimization Algorithm

The Internet of Things (IoT) has recently developed opportunities for various industries, including the petrochemical industry, that allow for intelligent manufacturing with real-time management and the analysis of the produced big data. In oil production, extracting oil reduces reservoir demand, causing oil supply to fall below the economically viable level. Gas lift is a popular artificial lift system that is both efficient and cost-effective. If gas supplies in the gas lift process are not limited, a sufficient amount of gas may be injected into the reservoir to reach the highest feasible production rate. Because of the limited supply of gas, it is essential to achieve the sustainable utilization of our limited resources and manage the injection rate of the gas into each well in order to enhance oil output while reducing gas injection. This study describes a novel IoT-based chemical reaction optimization (CRO) technique to solve the gas lift allocation issue. The CRO algorithm is inspired by the interaction of molecules with each other and achieving the lowest possible state of free energy from an unstable state. The CRO algorithm has excellent flexibility, enabling various operators to modify solutions and a favorable trade-off between intensification and diversity. A reasonably fast convergence rate serves as a powerful motivator to use as a solution. The extensive simulation and computational study have presented that the proposed method using CRO based on IoT systems significantly improves the overall oil production rate and reduces gas injection, energy consumption and cost compared to traditional algorithms. Therefore, it provides a more efficient system for the petroleum production industry.

A Comparison Among Random Search Algorithms for PID Controller Optimization

Random search algorithms have been widely applied in practice to solve optimization problems. In the field of automatic control, there has been much research applying random search algorithms to optimize the parameter of Proportional – Integral - Derivative (PID) controller. The content of this research mainly stops at comparing the performance of the PID controller using the traditional tuning method with the optimization algorithm-based tuning method. Therefore, there is a need for research to compare the performance among random search algorithms for finding optimal parameters of the PID controller. In this paper, authors simulate five well-known and modern stochastic search algorithms including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Chemical Reaction Optimization (CRO), Teaching Learning Based Optimization (TLBO) to find optimal parameters of the PID controller for controlling the speed of the DC motor. The simulation results showed that the CRO algorithm is the most suitable to optimize PID controller parameters for the proposed problem.

Maximum likelihood based estimation with quasi oppositional chemical reaction optimization algorithm for speech signal enhancement

Maximum likelihood based estimation with quasi oppositional chemical reaction optimization algorithm for speech signal enhancement

Cycle Time and Human Fatigue Minimization for Human-Robot Collaborative Assembly Cell

Human fatigue, which is one of the main causes of efficiency decline and health damage, cannot be overlooked in the task scheduling of HRC that is becoming one of the latest frontiers of manufacturing. In this letter, we proposed a task scheduling model that integrated micro-breaks within job-cycles to provide recovery from fatigue accumulation by taking advantage of the characteristic of HRC. Furthermore, an improved Chemical Reaction Optimization (CRO) algorithm is developed to solve the trade-off between cycle time and human fatigue. Greedy strategy is applied in the global and local search reaction of CRO to solve the multi-objective problem which includes two conflicting goals of cycle time and fatigue minimization. The proposed model and algorithm are demonstrated on assembly cases inspired by an industry application. The results from the case study suggest that the proposed model outperforms the comparison model. Finally, computational results have demonstrated that the superiority of the proposed algorithm is proved by comparing with genetic algorithm (GA), imperialist competitive algorithm (ICA), and standard CRO.

Sustainable multi-products delivery routing network design for two-echelon supplier selection problem in B2B e-commerce platform

This paper examines the environmental impact produced by multi-vehicle transportation on a sustainable supply chain (SC) network. The relevance of green principles is gaining momentum day by day, which has forced the governments to introduce carbon emission schemes for the transportation associated with the firms. Various countries around the globe are introducing carbon-pricing schemes, in which a carbon tax is imposed based on the amount of anthropogenic emissions. A firm, which sets environmental standards for the emission associated with its operational activities, should design a transportation network based on the trade-off between its economic efficiency and the carbon emission. In this paper, the main focus is to design a sustainable supply chain network. A mixed-integer-non-linear-programming (MINLP) model is formulated to minimize the overall cost incurred in a multi-vehicle, multi-product sustainable transportation network. The meta-heuristic approach i.e., Hybrid Chemical Reaction Optimization Algorithm with Tabu search (CRO-TS) and LINGO solver have been used to solve the proposed model. This analysis can guide the government to encourage the logistics service providers to capitalize on anthropogenic gas emission systems and simultaneously design the tax policy on carbon emission.

Distributed Flexible Job-Shop Scheduling Problem Based on Hybrid Chemical Reaction Optimization Algorithm

Economic globalization has transformed many manufacturing enterprises from a single-plant production mode to a multi-plant cooperative production mode. The distributed flexible job-shop scheduling problem (DFJSP) has become a research hot topic in the field of scheduling because its production is closer to reality. The research of DFJSP is of great significance to the organization and management of actual production process. To solve the heterogeneous DFJSP with minimal completion time, a hybrid chemical reaction optimization (HCRO) algorithm is proposed in this paper. Firstly, a novel encoding-decoding method for flexible manufacturing unit (FMU) is designed. Secondly, half of initial populations are generated by scheduling rule. Combined with the new solution acceptance method of simulated annealing (SA) algorithm, an improved method of critical-FMU is designed to improve the global and local search ability of the algorithm. Finally, the elitist selection strategy and the orthogonal experimental method are introduced to the algorithm to improve the convergence speed and optimize the algorithm parameters. In the experimental part, the effectiveness of the simulated annealing algorithm and the critical-FMU refinement methods is firstly verified. Secondly, in the comparison with other existing algorithms, the proposed optimal scheduling algorithm is not only effective in homogeneous FMUs examples, but also superior to existing algorithms in heterogeneous FMUs arithmetic cases.

An Opposition-Based Learning CRO Algorithm for Solving the Shortest Common Supersequence Problem.

As a non-deterministic polynomial hard (NP-hard) problem, the shortest common supersequence (SCS) problem is normally solved by heuristic or metaheuristic algorithms. One type of metaheuristic algorithms that has relatively good performance for solving SCS problems is the chemical reaction optimization (CRO) algorithm. Several CRO-based proposals exist; however, they face such problems as unstable molecular population quality, uneven distribution, and local optimum (premature) solutions. To overcome these problems, we propose a new approach for the search mechanism of CRO-based algorithms. It combines the opposition-based learning (OBL) mechanism with the previously studied improved chemical reaction optimization (IMCRO) algorithm. This upgraded version is dubbed OBLIMCRO. In its initialization phase, the opposite population is constructed from a random population based on OBL; then, the initial population is generated by selecting molecules with the lowest potential energy from the random and opposite populations. In the iterative phase, reaction operators create new molecules, where the final population update is performed. Experiments show that the average running time of OBLIMCRO is more than 50% less than the average running time of CRO_SCS and its baseline algorithm, IMCRO, for the desoxyribonucleic acid (DNA) and protein datasets.

A hybrid algorithm based on modified chemical reaction optimization and best-first search algorithm for solving minimum vertex cover problem

A hybrid algorithm based on modified chemical reaction optimization and best-first search algorithm for solving minimum vertex cover problem