International Journal of Mathematical Modelling and Numerical Optimisation (5 papers in press)

Regular Issues

• A review of 0-1 knapsack problem by nature-inspired optimisation algorithms  
  by Ruchi Chauhan, Nirmala Sharma, Harish Sharma 
  Abstract: Nature is the origin of all the knowledge. Researches have built nature-inspired optimisation (NIO) algorithms, that follow natural principles to find solutions, for the real life problems. In the binary knapsack problem (0/1KP), a bag (or a knapsack) has to be filled with articles, where each article has a weight and a profit value, the articles are filled in the knapsack, in whole numbers, up to a weight limit, to attain the optimum profit. The 0/1KP does the optimum sub-structure selection from a given set of articles, i.e., there can be different optimum solutions for a given 0/1KP. The aim of this research is to discuss the NIO algorithms innovated for solving the 0/1KP. The review creates foundation, for future research on optimising the 0/1KP, from meta-heuristic NIO techniques.
  Keywords: nature-inspired optimisation; NIO; 0-1 knapsack problem; 0/1KP; NP-hard problems; swarm intelligence.
  DOI: 10.1504/IJSI.2022.10051132
   
  
• Retrospection and investigation of ANN-based MPPT technique in comparison with soft computing-based MPPT techniques for PV solar and wind energy generation system  
  by Sunita Chahar, Dinesh Kumar Yadav 
  Abstract: This article discusses the previously available research and summarises the state of knowledge of soft computing artificial neural network (ANN)-based control techniques for renewable energy systems. In recent years, wind and photovoltaic (PV) solar energy systems have been developed as key renewable energy sources. The main issue is to operate these energy sources for maximum power output in abrupt changes in environmental conditions. Besides different types of conventional control techniques, the soft computing-based control system has proved efficient in extracting the highest available output. There are few articles are available in the literature on ANN-based control systems in wind energy systems, however, sufficient research has been carried out for the ANN-based maximum power extraction techniques for PV solar. This article highlights the important features such as better controllability and performance of ANN-based control techniques in comparison with the other types of soft computing-based-tactics for PV solar and wind energy systems.
  Keywords: traditional algorithm; novel algorithm; hybrid algorithm; artificial neural network; ANN; solar photovoltaic; wind; renewable; maximum power point tracking.
  DOI: 10.1504/IJSI.2023.10055513
   
  
• Bio-inspired mix design optimization of self-compacting concrete using machine learning algorithms  
  by Sriman Pankaj B, Vasan A, Jabez Christopher J 
  Abstract: This study focuses on optimising concrete mix design using a hybrid bio-inspired optimisation algorithm that combines differential evolution (DE) and cuckoo search (CS). The study also evaluates the performance of two strength prediction models, artificial neural networks (ANNs) and support vector machine regression (SVR), in determining optimal mix proportions. The hybrid algorithm is tested using 11 benchmark test functions and the best approach is chosen to solve a mix design optimisation problem with the objectives of maximising compressive strength, minimising carbon emissions, and minimising cost. Results show that ANN outperforms SVR in terms of compressive strength, with a 30% increase observed. Both prediction models produce optimal mix proportions with minimal variation for cost and embodied carbon minimisation scenarios. The study demonstrates the efficacy of the hybrid optimisation algorithm in conjunction with a prediction model in determining optimal concrete mix proportions.
  Keywords: bio-inspired optimisation; swarm intelligence algorithms; machine learning; compressive strength prediction model; concrete mix design optimisation; cuckoo search; differential evolution.
  DOI: 10.1504/IJMMNO.2024.10058653
   
  
• Startup of oscillating heat pipes via Hopf bifurcation  
  by Carmen Chicone, Z.C. Feng, Stephen Lombardo, David G. Retzloff 
  Abstract: Phase changes occur in oscillating heat pipes (OHPs) inside a tube partially filled with liquid that loops through the hot and cold zones of the device. Evaporation occurs in the hot zone, condensation in the cold zone. Their net effect would intuitively lead to accumulation of liquid slugs in the cold zone and flow stagnation. In recent work, however, self-oscillations observed in a single-branch heat pipe are explained as self-excited mechanical resonator motion. We extend their analysis to typical OHP geometry. Based on a model that combines slug dynamics with a phenomenological model of evaporation, linear stability of the equilibrium corresponding to liquid slugs filling up the cold zone of the heat exchanger is analysed. Our results reveal relations among the system parameters that determine stability and oscillatory behaviour via Hopf bifurcations. Thus, an explanation is proposed for successful start-up - one of the grand challenges for OHP design.
  Keywords: oscillating heat pipe; mathematical model; linearisation; Hopf bifurcation; start-up; model validation.
  DOI: 10.1504/IJMMNO.2024.10059141
   
  
• A numerical analysis of temperature variation in a breast tumour with varying ages  
  by Sharmila Shrestha, Dil Bahadur Gurung, Gokul K. C. 
  Abstract: Breast cells grow abnormally and uncontrollably leading to the development of breast tumours. Tumour size is determined by its age. Tumours develop quickly in the early stages of growth and slowly after 250 days (Gautherie, 1980). New blood vessels sprout for the fulfillment of nutrients and oxygen when a tumour expands. These new vessels increase blood flow and metabolism in tumour than in healthy tissue. This increases the tumour’s temperature. At a particular tumour age, the temperature distribution in the breast has been investigated by some researchers Afify and Osman (1988) and Ng and Sudarshan (2001); however, no research has been done throughout breast tumour development. To approximate the temperature change of breast tumour, the finite element approach is applied. The temperature variation of breast tumour is observed over a period of 50 to 700 days at various blood perfusion and metabolic rate. The tumour temperature is maximum at 50 days and drops gradually. Since blood flow and metabolic rate both decline with age, the temperature does not vary as the tumour grows. However, the temperature of breast skin surface is increased with tumour age.
  Keywords: tumour age; female breast; temperature variation; FEM.
  DOI: 10.1504/IJMMNO.2024.10065151