A novel approach for optimization of electrically serial two-stage thermoelectric refrigeration systems using chemical reaction optimization (CRO) algorithm

A hybrid algorithm based on particle swarm and chemical reaction optimization for multi-object problems

In this paper, we propose a novel discrete chemical-reaction optimization (DCRO) algorithm for solving the flexible job shop scheduling problem with three objectives. The molecule is used to represent a solution. The four elementary reactions, i.e., the on-wall ineffective collision, the decomposition, the inter-molecular ineffective collision, and the synthesis, are used as the operators for the hybrid algorithm. In the hybrid algorithm, the crossover operator is embedded to learn information among molecules. To increase the ability to escape from the local optima, the buffer is used as the energy center to share kinetic energy among molecules. Experimental results on the well-known benchmarks show the efficiency and effectiveness of the proposed algorithm.

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).

This paper presents a comparison between the performance of Chemical Reaction Optimization algorithm and Genetic algorithm in solving maximum flow problem with the performance of Ford-Fulkerson algorithm in that. The algorithms have been implemented sequentially using JAVA programming language, and executed to find maximum flow problem using different network size. Ford-Fulkerson algorithm which is based on the idea of finding augmenting path is the most popular algorithm used to find maximum flow value but its time complexity is high. The main aim of this study is to determine which algorithm will give results closer to the Ford-Fulkerson results in less time and with the same degree of accuracy. The results showed that both algorithms can solve Max Flow problem with accuracy results close to Ford Fulkerson results, with a better performance achieved when using the genetic algorithm in term of time and accuracy.

Finding maximum independent set (MIS) in a graph is considered one of the fundamental problems in the computer science field, where it can be used to provide solutions for various real life applications. For example, it can be used to provide solutions in scheduling and prioritization problems. Unfortunately, this problem is one of the NP-problems of computer science, which limit its usage in providing solution for such problems with large sizes. This leads the scientists to find a way to provide solutions of such problems using fast algorithms to provide some near optimal solutions. One of the techniques used to provide solutions is to use metaheuristic algorithms. In this paper, a metaheuristic algorithm based on Chemical Reaction Optimization (CRO) is applied with various techniques to find MIS for application represented by a graph. The suggested CRO algorithm achieves accuracy percentages that reach 100% in some cases. This variation depends on the overall structure of the graph along with the picked parameters and colliding molecule selection criteria during the reaction operations of the CRO algorithm.

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.

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.

Comparison of the optimal performance of single- and two-stage thermoelectric refrigeration systems

Two Stage Indian Food Grain Supply Chain Network Transportation-Allocation Model

The chemical reaction optimization algorithm is one of the most popular meta-heuristic population based algorithms, which is applied into many combinational optimization problems. The deteriorating effect in flow shop scheduling have gained an increasing concern from the manufacturing system and combinational optimization areas. However, Most studies focus on the two and three machines. In this paper, we propose a general flow shop scheduling problem, and a hybrid chemical reaction optimization algorithm is proposed to solve this problem. In the proposed algo­rithm, the newly generated molecules are constructed based on the better solutions found and injected to the population adaptively, which can enhance and balance the exploration and exploitation ability. Simulation experiments are carried out on a set of stochastic test problems with the different size. Through the analysis of experimental results, the proposed algorithm performs better than the compared algorithms in solving the proposed problem.

Maritime mobile edge computing (MMEC) technology facilitates the deployment of computationally intensive object detection tasks on Maritime Internet of Things (MIoT) devices with limited computing resources. However, the dynamic marine network and environmental interference in feature extraction adversely affect detection accuracy and cause delay. In this paper, we propose a cumulative confidence-driven joint scheduling strategy for image detection tasks in MMEC scenarios. The strategy employs lightweight and full models as the detection framework. Through an adaptive decision-making scheme for marine device image recognition, the proposed strategy accumulates results from different models within the framework to ensure quality of service (QoS). To obtain a dynamic offloading strategy that minimizes the total system cost of latency and energy consumption, the problem is divided into two subproblems, and a chemical reaction optimization algorithm is used to reduce the computational complexity. Then, a state normalization action project deep deterministic policy gradient (SNAP-DDPG) algorithm is proposed to handle environmental dynamics, achieving minimized system cost with satisfied QoS. The simulation results indicate that, compared to existing algorithms, the proposed SNAP-DDPG algorithm keeps object detection confidence, with latency reduced by 34.78%.

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

The thermoelectric refrigeration system is an application of the Peltier effect, and good refrigeration performance is dependent on effective heat dissipation performance. To enhance the cooling performance of the thermoelectric system, this paper designs a new type of finned heat sink, which does not change the overall size of the thermoelectric system. The performance of the refrigeration system under the new fin is tested by experiments under various conditions. During the experiment, the cooling wind speed, the temperature of the hot and cold side of the TEC, the power consumption of the fan, and other parameters were directly recorded through the measuring instrument. The results show that the use of new finned heat sinks can improve the COP of the thermoelectric refrigeration system. Within the scope of the study, the thermal resistance of the new fins can be reduced by 42.6%, and the system COP value can be increased by 22.8%. In addition, increasing the cooling wind speed can further reduce the cold side temperature. Within the research range, the lowest temperature can reach −8.25 °C, but the power consumed by the fan is 166% of that of the conventional fin heat sink refrigeration device.

Layout exploration for three-dimensional Networks-on-Chip: Chemical equilibrium simulation approach

The maximum independent set (MIS) problem is a classical NP-hard problem that appears in diverse applications such as scheduling, wireless sensor networks, resource allocation, and bioinformatics. Metaheuristic approaches, including Chemical Reaction Optimization (CRO), have shown promising results on medium-sized instances; however, plain CRO typically relies on stochastic structural modifications and may require multiple runs to reach optimal or near-optimal independent sets. This paper proposes a hybrid (memetic) variant of CRO for the MIS problem in which each newly generated molecule is passed through a problem-specific local refinement procedure. The local step greedily expands partial independent sets and applies 1-swap/2-swap neighborhood moves to remove conflicts and increase the cardinality of the set. By combining the global exploration capability of CRO with the intensification of local search, the proposed method achieves higher-quality solutions, especially on dense graphs where conflicts are frequent. Experimental results on synthetic graphs of varying sizes and densities, as well as on standard benchmark instances, show that the hybrid CRO consistently outperforms the baseline CRO in terms of best solution, average solution, and number of runs needed to reach the best MIS. These findings indicate that embedding domain knowledge into CRO is an effective way to improve robustness without losing generality.