Forthcoming and Online First Articles

International Journal of Swarm Intelligence

International Journal of Swarm Intelligence (IJSI)

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International Journal of Swarm Intelligence (6 papers in press)

Regular Issues

  • A coupled multi-linear regression and genetic algorithm-based modelling and optimisation of surface roughness in machining of brass   Order a copy of this article
    by Suhail Ahmed Manroo, Suhail Ganiny 
    Abstract: In this paper, the authors present a multi-linear regression-based approach for the modelling of surface roughness during the turning of a commercial brass alloy. Three regression models are developed by utilising the experimental data gathered following a full-factorial-based design-of-experiments (DoEs) methodology. While the conventional practice has been to develop regression models using the entire experimental datasets, we deviate from the same and employ only a subset of the available data for the purpose, the remaining data being used for the model validation. The results obtained herein reveal that the second order regression model is statistically better than the other two in predicting the surface roughness for both the datasets. The global minimum surface roughness is determined by using the developed regression models in conjunction with the genetic algorithm-based single objective optimisation. The regression models serve as candidate objective functions for the genetic algorithm. The optimisation results reveal that the global minimum obtained using the second order regression model is in close agreement (accuracy - 94%) with the experimentally obtained minimum surface roughness and thus reaffirms the effectiveness of the second order regression model in predicting the surface roughness of brass during turning operation.
    Keywords: surface roughness; modelling; multi-linear regression; optimisation; genetic algorithm.
    DOI: 10.1504/IJSI.2021.10039928
  • MPPT optimisation techniques and power electronics for renewable energy systems: wind and solar energy systems   Order a copy of this article
    by Abrar Ahmed Chhipa, Shripati Vyas, Vinod KumarKumar, R.R. Joshi 
    Abstract: This study proposes the challenges faced by the present renewable energy scenario and the contribution of power electronics and maximum power point tracking (MPPT) optimisation techniques. India is one of the leading energy harvester country in the world. Power electronics keep power system operation stable, harvest electric power from renewable energy sources (RESs), and reduce energy consumption. This study has focused on power electronics and MPPT optimisation techniques for wind energy and photovoltaic energy and points out some aspects related to configuration for integration, energy storage technologies, reliability, and grid connection. Apart from this, modern optimisation techniques for MPPT control using artificial intelligence like fuzzy control and neural network control are also presented in this study.
    Keywords: energy optimisation; optimisation techniques; renewable energy; wind energy; solar energy; energy storage; maximum power point tracking; MPPT; wind MPPT; solar MPPT; power electronics; artificial intelligence; fuzzy logic; neural network.
    DOI: 10.1504/IJSI.2021.10041290
  • Optimisation of EDM process parameters by application of genetic algorithm   Order a copy of this article
    by Sumit Sharma, Love Kishore Sharma, Dheeraj Joshi, Mohammad Israr, Ritesh Mathur, Deepak Sharma 
    Abstract: The EDM efficiency can be strengthened by suitably defining the critical factors and their values for obtaining desired responses. In the present research work, response surface methodology (RSM) with face centred cubic (FCC) approach is implemented for correlating the response, i.e., material removal rate (MRR) with control factors, i.e., voltage, peak current, pulse on time. These factors provide maximum MRR of 230.6 gm/min. To obtain the optimal solution, genetic algorithm (GA) is coupled with the obtained mathematical model. The dielectric fluid used is kerosene oil. ANOVA and F-test are used to check for model validation. R2 and adjusted R2 which confirms the validity of proposed model. The work piece material used in research work is AISI 4140 steel.
    Keywords: response surface methodology; RSM; face centred cubic; FCC; material removal rate; MRR; genetic algorithm; ANOVA.
    DOI: 10.1504/IJSI.2021.10037199
  • A comprehensive review on recent intelligent metaheuristic algorithms   Order a copy of this article
    by S. Rajalakshmi, S. Kanmani 
    Abstract: Metaheuristics is an interesting research area with significant advances in solving problems with optimisation. Substantial advancements in metaheuristic are being made, and various new algorithms are being developed every day. The analyses in this area will undoubtedly be helpful for future improvements. This paper's main objective is to conduct a literature review of some recent algorithms motivated by nature to compare their features. This paper reviews some recently published nature inspired algorithms such as squirrel search algorithm (SSA), improved squirrel search algorithm (ISSA), grey wolf optimiser (GWO) algorithm, random walk grey wolf optimiser (RW_GWO) algorithm, sailfish optimiser (SAO) algorithm, sandpiper optimisation algorithm (SOA), search and rescue operations (SRO) algorithm, slime mould optimisation (SMO) algorithm, grasshopper optimisation algorithm (GOA) and opposition based learning grasshopper optimisation algorithm (OBLGOA). This paper focuses on a brief introduction of these algorithms and key concepts involved in formulation of swarm intelligence. Finally, this work outlines the directions for conducting effective future research.
    Keywords: metaheuristics; optimisation; swarm intelligence; improved metaheuristics.
    DOI: 10.1504/IJSI.2021.10037176
  • Determination and optimisation of global grain transport areas   Order a copy of this article
    by Xinjie Shi, Bochen Zhang, Xiaoli Yang, Xusheng Kang 
    Abstract: At present, there are many defects in the global grain allocation, such as large grain losses and high consumption. Especially, the global grain allocation is particularly unbalanced, which leads to the phenomenon of 'world hunger' becoming more and more serious. This paper proposes establishing several grain transshipments globally to coordinate the global grain allocation. To alleviate the imbalance of grain allocation, this paper uses the per capita grain output and per capita GDP of each country to estimate the weight of grain allocation in each country by using the entropy weight method. Based on this weight and the geographical location of each country, all countries and regions of the world are divided into several grain transshipment sub-regions by using the clustering analysis method. For each grain transshipment sub-region, the advantages and disadvantages of the single-transshipment model and the double-transshipment model are compared by using intelligent algorithms such as the immune algorithm, and the optimal grain transshipment location for construction and implementation is obtained by manually adjusting the optimal longitude and latitude obtained by the algorithm. Finally, the validity of the model algorithm is verified by simulation.
    Keywords: grain transshipment location; entropy weight method; cluster analysis method; grain transshipment sub-regions; immune algorithm.
    DOI: 10.1504/IJSI.2022.10046860
  • Hybrid architecture for intelligent bidding in hourly-based electricity market   Order a copy of this article
    by Kavita Jain, Akash Saxena 
    Abstract: The paper presents a hybrid structure, that develops the foremost bidding strategies for an electrical power generating company (GenCo) for an hourly-based electricity market (EM). For a GenCo, to develop the most desirable bidding technique, an outcome of a two-level optimisation process is used. The two-levels of optimisation are as follows: at the primary level, the goal of GenCo is to strategically bid for maximum benefit, and the independent system operator (ISO) analyses the market clearing price (MCP) at the secondary level to help measure the quantity of energy dispatched for each GenCo to optimise global welfare. In this paper, we trained a neural network (NN) by using whale optimisation algorithm (WOA) to achieve a high hourly profit for GenCo. The efficacy of the proposed hybrid structure is decided via numerous case research on the benchmark IEEE 14-bus test system in an hourly-based market with a dynamically converting demand profile. For the GenCo, block bidding supply function is taken and optimal bidding technique is explored with changing demand. Strategic bidding is a method discovered inside an oligopoly EM that has many insinuations on the layout and policy-making of the mechanism.
    Keywords: electricity market; EM; oligopoly; strategic bidding; optimisation technique; whale optimisation algorithm; WOA.
    DOI: 10.1504/IJSI.2022.10046861