Particle swarm optimization (PSO). A tutorial

Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition

This paper presents an overview of the research progress in Particle Swarm Optimization (PSO) during 1995–2017. Fifty two papers have been reviewed. They have been categorized into nine categories based on various aspects. This technique has attracted many researchers because of its simplicity which led to many improvements and modifications of the basic PSO. Some researchers carried out the hybridization of PSO with other evolutionary techniques. This paper discusses the progress of PSO, its improvements, modifications and applications.

A Review of Particle Swarm Optimization

Over the ages, nature has constantly been a rich source of inspiration for science, with much still to discover about and learn from. Swarm Intelligence (SI), a major branch of artificial intelligence, was rendered to model the collective behavior of social swarms in nature. Ultimately, Particle Swarm Optimization algorithm (PSO) is arguably one of the most popular SI paradigms. Over the past two decades, PSO has been applied successfully, with good return as well, in a wide variety of fields of science and technology with a wider range of complex optimization problems, thereby occupying a prominent position in the optimization field. However, through in-depth studies, a number of problems with the algorithm have been detected and identified; e.g., issues regarding convergence, diversity, and stability. Consequently, since its birth in the mid-1990s, PSO has witnessed a myriad of enhancements, extensions, and variants in various aspects of the algorithm, specifically after the twentieth century, and the related research has therefore now reached an impressive state. In this paper, a rigorous yet systematic review is presented to organize and summarize the information on the PSO algorithm and the developments and trends of its most basic as well as of some of the very notable implementations that have been introduced recently, bearing in mind the coverage of paradigm, theory, hybridization, parallelization, complex optimization, and the diverse applications of the algorithm, making it more accessible. Ease for researchers to determine which PSO variant is currently best suited or to be invented for a given optimization problem or application. This up-to-date review also highlights the current pressing issues and intriguing open challenges haunting PSO, prompting scholars and researchers to conduct further research both on the theory and application of the algorithm in the forthcoming years.

Major Advances in Particle Swarm Optimization: Theory, Analysis, and Application

Rock-socketed piles are commonly used in foundations built in soft ground, and thus, their bearing capacity is a key issue of universal concern in research, design and construction. The accurate prediction of the ultimate bearing capacity (Qu) of rock-socketed piles is a difficult task due to the uncertainty surrounding the various factors that affect this capacity. This study was aimed at developing an artificial neural network (ANN) model, as well as a hybrid model based on both particle swarm optimisation (PSO) and ANN, with which to predict the Qu of rock-socketed piles. PSO, a powerful population-based algorithm used in solving continuous and discrete optimisation problems, was here employed as a robust global search algorithm to determine ANN weights and biases and thereby improve model performance. To achieve the study aims, 132 piles socketed in various rock types as part of the Klang Valley Mass Rapid Transit project, Malaysia, were investigated. Based on previous related investigations, parameters with the most influence on Qu were identified and utilised in the modelling procedure of the intelligent systems. After constructing and modelling these systems, selected performance indices including the coefficient of determination (R2), root-mean-square error, variance account for and total ranking were used to identify the best models and compare the obtained results. This analysis revealed that the hybrid PSO---ANN model offers a higher degree of accuracy compared to conventional ANN for predicting the Qu of rock-socketed piles. However, the developed model would be most useful in the preliminary stages of pile design and should be used with caution.

Developing a hybrid PSO---ANN model for estimating the ultimate bearing capacity of rock-socketed piles

Particle Swarm Optimization is a popular heuristic search algorithm which is inspired by the social learning of birds or fishes. It is a swarm intelligence technique for optimization developed by Eberhart and Kennedy [1] in 1995. Inertia weight is an important parameter in PSO, which significantly affects the convergence and exploration-exploitation trade-off in PSO process. Since inception of Inertia Weight in PSO, a large number of variations of Inertia Weight strategy have been proposed. In order to propose one or more than one Inertia Weight strategies which are efficient than others, this paper studies 15 relatively recent and popular Inertia Weight strategies and compares their performance on 05 optimization test problems.

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Inertia Weight strategies in Particle Swarm Optimization

Physical and chemical properties of protein help to determine the quality of protein structure. Here we explore the machine learning models using six physical and chemical properties, namely total empirical energy, secondary structure penalty, total surface area, pair number, residue length and Euclidean distance to predict the RMSD of a protein structure in the absence of its true native state. The Real Coded Genetic Algorithm is used to determine feature importance, and k-fold cross-validation is used to measure the robustness of the best predictive model. The experiments show that the random forest model outperforms the other machine learning approaches in RMSD prediction. The performance result shows that in the prediction of RMSD, the Root Mean Square Error RMSE is 0.48, correlation is 0.90, R² is 0.82 and accuracy is 97.02% with ±2 error on the testing data. The data set used in the study is available at http://bit.ly/PSP-ML.

Protein structure prediction RMSD ≤ 5 Å using machine learning models

The experiment was conducted at Research cum Instructional Farm, IGKV, Raipur (Chhattisgarh) during Kharif season 2015-2016. The soil of experimental field was sandy loam in texture (Inceptisols) known as Matasi. The Experiment was laid out in Factorial Randomized Block Design (FRBD) with three replications. The treatment consisted of three varieties viz. MTU-1010 (V1), Indira barani dhan (V2) and IR-64 (V3) and four weed management practices viz. ­Rinskor @ 37.5 ml a.i. ha-1 at 20 DAS (H1), H2 - Rinskor @ 75 ml a.i. ha-1 at 20 DAS, Mechanical weeding at 20 and 40 DAS (H3) and Control (H4). The results of experiment indicated that variety MTU-1010 (V1) registered significantly highest values of growth characters of rice like leaf area, plant population, dry matter accumulation and number of tillers. It was at par to variety IR-64 (V3) for above parameters. Among weed management practices, Mechanical weeding at 20 and 40 DAS (H3) registered significantly highest values of growth parameters of rice like plant population, leaf area, dry matter accumulation and number of tillers, but it was comparable to Rinskor @ 37.5 ml a.i. ha-1 at 20 DAS (H1). Among the varieties, higher grain yield and B:C ratio was recorded under MTU-1010 (V1) as regards to weed management practices maximum grain yield and B:C ratio were obtained under Mechanical weeding at 20 and 40 DAS (H3).

https://www.chemijournal.com/archives/2018/vol6issue1/PartAB/6-1-243-556.pdf

Effect of different doses of herbicides and mechanical weeding on yield attributes and grain yield of direct seeded rice (Oryza sativa L.) varieties under Inseptisols of Chhattisgarh plain

The present investigation entitled ‘‘Effect of Cyclanilide + Mepiquat Chloride on growth and productivity of Soybean [Glycine max (L.) Merrill]’’ was carried out at Instructional cum Research Farm, Indira Gandhi Krishi Vishwavidyalaya, Raipur during Kharif season of 2016. Experiment was laid out in Randomized Block Design (RBD) with three replications. The soybean variety JS-9752 tested under different plant growth regulator management. The result revealed that application of Cyclanilide 22 + Mepiquate Chloride 88 SC @ 100 ml ha-1 recorded the significantly highest plant height, number of branches plant-1, number of leaves plant-1, Leaf area index, seed yield (kg ha-1) Stover yield(kg ha-1) seed index (100-seed weight) and harvest index as compare to other treatment.

https://www.chemijournal.com/archives/2018/vol6issue1/PartAE/6-1-418-571.pdf

Effect of Cyclanilide + Mepiquat chloride on growth and productivity of soybean [Glycine max (L.) Merrill]

A field experiment was conducted during kharif season of 2015 at the Research cum Instructional Farm, Indira Gandhi Krishi Vishwavidyalaya, Raipur to evaluate the effect of foliar nutrition on uptake of nutrients and economics of Soybean [Glycine max (L.) Merrill] under Vertisols of Chhattisgarh plains. The experiment was laid out in Randomized Block Design with three replications. The results revealed that application of RDF+spray of DAP @ 2% at pod initiations stages of crop growth resulted significantly higher uptake of nutrients N, P, K, and also positive effect on micronutrients and economics compared to the application of RDF+Water spray at pod initiation stage and RDF only. It also positive effect on micronutrients in soil such as activity of microbial population, improve seed quality and oil quality of soybean through regulate nutrient during pod filling stage when the soil are not able to provide nutrients, quality parameters and nutrient uptake of soybean were significantly influenced by foliar application of nutrients at different stages of crop. The foliar application of RDF+spray of DAP @ 2% recorded significantly higher net return of ` 60,279 with. B:C ratio of 3.07. Foliar spraying with 2% DAP at flowering and at early pod development in addition to soil application of NPK fertilizer was found to be more beneficial to improve the productivity of soybean than RDF application alone.

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Paramjeet Singh

Papers

Inertia Weight strategies in Particle Swarm Optimization

Protein structure prediction RMSD ≤ 5 Å using machine learning models

Effect of different doses of herbicides and mechanical weeding on yield attributes and grain yield of direct seeded rice (Oryza sativa L.) varieties under Inseptisols of Chhattisgarh plain

Effect of Cyclanilide + Mepiquat chloride on growth and productivity of soybean [Glycine max (L.) Merrill]

Effect of Foliar Nutrition on Uptake of Nutrients and Economics of Soybean