Applying a Multiobjective Gravitational Search Algorithm (MO-GSA) to Discover Motifs

Gravitational search algorithm (GSA) is a swarm intelligence-based algorithm which is inspired from the law of motion and law of gravity. GSA leads to the loss of the exploitation capability. To find a trade-off between exploration and exploitation capabilities of GSA, a modified gravitational search algorithm is proposed namely exponent inertia weight-based GSA (EIWGSA). The proposed algorithm maintains a proper balance between the exploitation and exploration skills of GSA by introducing an exponent inertia weight (EIW) parameter. The proposed algorithm is implemented over 15 benchmark functions and compared with basic GSA, BBO and PSO algorithm. Then, the MGSA algorithm is applied to design of PID controller for the magnetic leviation system over a wide difference operating air gap as 3 mm, 10 mm and 17 mm.

A novel multi objective Loading Pattern Optimization by Gravitational Search Algorithm (GSA) for WWER1000 core

The laws of gravity and mass interactions inspire the gravitational search algorithm (GSA), which nds optimal regions of complex search spaces through the interaction of individuals in a population of particles. Although GSA has proven effective in both science and engineering, it is still easy to suffer from premature convergence especially facing complex problems. In this paper, we pro- posed a new hybrid algorithm by integrating genetic algorithm (GA) and GSA (GA-GSA) to avoid premature convergence and to improve the search ability of GSA. In GA-GSA, crossover and mutation operators are introduced from GA to GSA for jumping out of the local optima. To demonstrate the search ability of the proposed GA-GSA, 23 complex benchmark test functions were employed, including unimodal and multimodal high-dimensional test functions as well as multimodal test functions with xed dimensions. Wilcoxon signed-rank tests were also utilized to execute statistical analysis of the results obtained by PSO, GSA, and GA-GSA. Experimental results demonstrated that the proposed algorithm is both efficient and effective.

Deoxyribonucleic acid (DNA) has become one of the most examined molecules on the planet. Scientist around the world have been trying to unravel its secrets for many purposes. For example, genetic information is currently used to raise better plants and animals, create enhanced pharmaceuticals for humans, and for gene therapy in medicine. Science as a whole has benefited from the study of genetics because of the increased understanding of biological process that all organisms share. In recent decades, a significant amount of research has been directed towards sequencing and understanding the entire human genome through the Human Genome Project (HGP) launched in 1986. The goal of the HGP was to find the location of the approximately 1×105 human genes, and read all the sequence of human genome (about 3×109 base pairs, bp). An exponential grow rate of that research has resulted in reaching the goal by 2003. Similarly, the speed of finding genes and their locations is also increasing rapidly. On the other hand, the traditional methods of finding genes and their location at chromatosomes through testing their biological function have been inherently slow. Although numerous faster techniques have been developed, there is still a need to augment them with new approaches. Therefore, robust computational solutions to the gene-finding problem could provide a valuable resource for the HGP and for the molecular-biology community. Most of the current research in the deciphering the meaning of DNA sequences is approached from the lowest base-pair level. Its main objective is to search for patterns or correlations existing in the DNA sequence related to codons, amino acids, and proteins. A number of gene-finding systems have been developed in recent decades. These systems use a variety of sophisticated computational data-miming techniques, including neural networks, dynamic programming, rule-based methods, decision trees, probability reasoning, hidden Markov chains, genetic programming, and support vector machines. Most of these approaches are based on local measures only. In addition, many of the techniques rely on the statistical qualities of exons in the gene, thus using only the known gene pool as a training set for their classification. Although the techniques have demonstrated limited success, better techniques should be developed. An approach to finding such improved techniques is to consider long-range relations (in addition to short-range relations) in the DNA sequence, spanning 104 nucleotides. If we had a good technique to measure such long-range relations, we would be able to estimate any existing self-affinity (fractality) in the DNA sequence, without any a priori assumptions about its structure. This would be a data-driven approach, rather than the common modeldriven approach. Along those lines, preliminary results have already been reported in the literature on a local self-similarity with a 180 bp periodicity in mammalian nuclear DNA sequence. Other publications have provided evidence that the long-range fractal correlations appear in DNA sequences with different values in different regions of the sequence. This paper describes such a multiscale approach, together with an algorithm based on a multifractal analysis, and demonstrates that multifractal estimates can be used to characterize DNA sequences [1], [2], [3]. This multifractal approach appears to be new, and may provide a key to cognitive analysis of DNA sequences. It should be clear that the DNA sequencing and gene finding techniques constitute a subset of bioinformatics, the science of using information to understand biology, with its numerous tools. In turn, bioinformatics is a subset of computational biology which is the application of quantitative analytical techniques in modelling biological systems. Very often, for structural biologists, DNA is not just a sequence of symbols, but implies 3D structures, molecular shapes and conformational changes, active sites, chemical reactions, and intermolecular interactions, each with a hierarchy of importance. What might be missing from this web of various approaches to solving life-related problems is cognitive informatics with its new approaches [4].

Gravitational search algorithm (GSA) is a swarm intelligence-based algorithm which is inspired from the law of motion and law of gravity. GSA leads to the loss of the exploitation capability. To find a trade-off between exploration and exploitation capabilities of GSA, a modified gravitational search algorithm is proposed namely exponent inertia weight-based GSA (EIWGSA). The proposed algorithm maintains a proper balance between the exploitation and exploration skills of GSA by introducing an exponent inertia weight (EIW) parameter. The proposed algorithm is implemented over 15 benchmark functions and compared with basic GSA, BBO and PSO algorithm. Then, the MGSA algorithm is applied to design of PID controller for the magnetic leviation system over a wide difference operating air gap as 3 mm, 10 mm and 17 mm.

This paper present a new approach for edge detection using gravitational search algorithm (GSA) and universal law of gravity. The direct application of any edge detection algorithm on an entire image requires a huge memory which leads to optimization problems. Gravitational search algorithm is an optimization algorithm inspired by Newtonian gravity. Objects in space attract each other by way of gravity force, and this force causes a global movement of all objects towards the objects with heavier masses. Hence, masses cooperate using a direct form of communication, through gravitational force. In the proposed approach the edges are detected using the law of universal gravity and movement of agents are computed using gravitational search algorithm. The proposed approach is able to detect the edge pixel in an image up to a certain extent. The technique can be further extended for finding more edge pixels by modifying the gravitational search algorithm.

Inspiring by nature have motivated many researchers in many fields of sciences and engineering. The Gravitational search algorithm (GSA) is a recent created metaheuristic algorithm by using law of gravity and mass interactions. In this paper, a new operator inspired by some of the characteristics of the black hole as an astronomy phenomenon for GSA is presented. When a star is converted to a black hole under situations, it has the extremely strong gravity that prevents anything to escape from, and the objects that are closed to the black hole, experience very strong force called tidal force which it causes to collapse them to the black hole. We propose a new operator using these features and hybridize it with GSA (BH-GSA) in order to prevent facing the premature convergence and to improve the abilities of GSA in exploration and exploitation. The proposed algorithm is applied to two sets of standard benchmark functions. The first set includes 23 standard benchmark functions and in this set the performance of the proposed algorithm is compared with the standard GSA, the disruption GSA, the particle swarm optimization (PSO), and the real genetic algorithm (GA). The second set contains the CEC 2005 benchmark functions. In this set, we compare the BH-GSA with some well-known metaheuristic algorithms. The obtained results and comparing with the competing algorithms prove that the BH-GSA has merit in the field of continuous space optimization.

Gravitational Search Algorithm (GSA) is a recent metaheuristic algorithm inspired by Newton’s law of gravity and law of motion. In this search process, position change is based on the calculation of step size which depends upon a constant namely, Gravitational Constant (G). G is an exponentially decreasing function throughout the search process. Further, in-spite of having different masses, the value of G remains same for each agent, which may cause inappropriate step size of agents for the next move, and thus leads the swarm toward stagnation or sometimes skipping the true optima. To overcome stagnation, we first propose a gravitational constant having different scaling characteristics for different phase of the search process. Secondly, a dynamic behavior is introduced in this proposed gravitational constant which varies according to the fitness of the agents. Due to this behavior, the gravitational constant will be different for every agent based on its fitness and thus will help in controlling the acceleration and step sizes of the agents which further improve exploration and exploitation of the solution search space. The proposed strategy is tested over 23 well-known classical benchmark functions and 11 shifted and biased benchmark functions. Various statistical analyzes and a comparative study with original GSA, Chaos-based GSA (CGSA), Bio-geography Based Optimization (BBO) and DBBO has been carried out.

Gravitational search algorithm (GSA) is a newly developed and promising algorithm based on the law of gravity and interaction between masses. This paper proposes an improved gravitational search algorithm (IGSA) to improve the performance of the GSA, and first applies it to the field of dynamic neural network identification. The IGSA uses trial-and-error method to update the optimal agent during the whole search process. And in the late period of the search, it changes the orbit of the poor agent and searches the optimal agent’s position further using the coordinate descent method. For the experimental verification of the proposed algorithm, both GSA and IGSA are testified on a suite of four well-known benchmark functions and their complexities are compared. It is shown that IGSA has much better efficiency, optimization precision, convergence rate and robustness than GSA. Thereafter, the IGSA is applied to the nonlinear autoregressive exogenous (NARX) recurrent neural network identification for a magnetic levitation system. Compared with the system identification based on gravitational search algorithm neural network (GSANN) and other conventional methods like BPNN and GANN, the proposed algorithm shows the best performance.

Motif finding problems, abstracted as the planted (l, d)-motif finding problem, are a major task in molecular biology--finding functioning units and genes. In 2002, the random projection algorithm was introduced to solve the challenging (15, 4)-motif finding problem by using randomly chosen templates. Two years later, a so-called uniform projection algorithm was developed to improve the random projection algorithm by means of low-dispersion sequences generated by coverings. In this article, we introduce an improved projection algorithm called the low-dispersion projection algorithm, which uses low-dispersion sequences generated by developed almost difference families. Compared with the random projection algorithm, the low-dispersion projection algorithm can solve the (l, d)-motif finding problem with fewer templates without decreasing the success rate.

Technology is transforming rapidly. Security during data transmission is an increasingly critical and essential factor for the integrity and confidentiality of data in the financial domain, such as e-commerce transactions and bank transactions, etc. We cannot overestimate the importance of encryption/decryption of information in the digital economy. The need to strengthen and secure the digital economy is urgent. Cryptography maintains the security and integrity of data kept on computers and data communicated over the internet using encryption/decryption. A new concept in cryptography named DNA cryptography has attracted the interest of information security professionals. The DNA cryptography method hides data using a DNA sequence, with DNA encryption converting binary data into the DNA sequence. Deoxy Ribonucleic Acid (DNA) is a long polymer strand having nitrogen bases adenine (A), thymine (T), cytosine (C), and guanine (G), which play an important role in plain text encoding and decoding. DNA has high storage capacity, fast processing, and high computation capacity, and is more secure than other cryptography algorithms. DNA cryptography supports both symmetric and asymmetric cryptography. DNA cryptography can encrypt numeric values, English language and unicast. The main aim of this paper is to explain different aspects of DNA cryptography and how it works. We also compare different DNA algorithms/methods proposed in a previous paper, and implement DNA cryptography using one-time pad (OTP) and amino acid sequence using java language. OTP is used for symmetric key generation and the DNA sequence is converted to an amino acid sequence to create confusion.

Gravitational search algorithm (GSA) is a meta-heuristic search algorithm based on the Newton's gravity law. In this article, a new variant of GSA is introduced, namely Accelerative Gravitational Search Algorithm (AGSA). In the proposed AGSA, an acceleration coefficient is introduced in the velocity update equation to control the acceleration of the individual. In the proposed position update process individuals are allowed to explore the search space in early iterations while exploitation in later iterations. The reliability, robustness and accuracy of the proposed algorithm is measured through various statistical analyses over 12 complex test problems. To show the competitiveness of the proposed strategy, the reported results are compared with the results of GSA, Biogeography Based Optimization (BBO), and Differential Evaluation (DE) algorithms.

The regulation of gene expression is controlled by multiple mechanisms. The primary level of control is embedded within the sequence itself, as the transcription factors bind specific deoxyribonucleic acid (DNA) sequences at the gene regulatory regions and either activate or silence the genes. A secondary level of control includes enhancer sequences, which may lie megabases away from the gene and are necessary for the induction of gene expression in the human genome 1. A third level of control is the three-dimensional structure of chromatin, which can bring enhancers and promoters into contact with one another and can be restricted by the CCCTC-binding factor (CTCF) that insulates chromatin segments into topologically associated domains. A number of novel methodological approaches have recently hastened characterisation of the regulatory elements in the genome 2. One commonly used approach is to couple the chromatin immunoprecipitation sequencing of the transcription factors with gene expression profiling to study the transcription factor fusion targets in leukaemia 3, 4. In comparison, the assay named global nuclear run-on sequencing allows simultaneous exploration of the dynamics of gene expression and regulatory element activity. In a cell model with inducible ETS variant 6-runt-related transcription factor 1 (ETV6-RUNX1) fusion, we were able to follow up the interplay of the enhancer elements and their host genes that occur downstream of this structural variation, which is the most common one found in childhood leukaemias 5. The ETV6-RUNX1 fusion bound the predominantly RUNX1 motif containing DNA sequences and caused repression of the enhancer activity as measured by the enhancer ribonucleic acid (RNA) levels. Interestingly, in most cases, the repression at the enhancers led to lower transcription at the nearby gene loci. In particular, the ETV6-RUNX1 aberration predominantly altered the expression of genes regulating B-cell adhesion and signalling. Furthermore, we noted a significantly higher repression of cluster of differentiation (CD)19/20 associated super-enhancers, in comparison with stem cell-linked CD34 super-enhancers 5. Super-enhancers are regions of the genome with clusterings of multiple enhancer sites and are deemed essential for lineage specification 6. They could provide a possible explanation for the disruption of B-cell differentiation, which is a hallmark of precursor leukaemias. A significant number of somatic mutations in leukaemia are located outside the coding regions of the genome. Mansour et al. 7 identified a small insertional mutation in a regulatory element at the T-cell acute lymphocytic leukaemia protein 1 (TAL1) gene locus in patients with T-cell acute lymphoblastic leukaemia. The mutation introduced a novel transcription factor binding site, for transcription factor myeloblastosis (MYB), thereby creating an aberrant super-enhancer that led to gross overexpression of the TAL1 gene. Such changes at the binding sites may also be inherited, as exemplified by the risk of neuroblastomas associated with a common single nucleotide polymorphism that alters a site where GATA transcription factors bind with DNA in a super-enhancer region of the LIM domain-only 1 (LMO1) gene 8. Moreover, the regulatory boundaries at the oncogene loci may be lost during disruption of the CTCF binding sites by mutations, as shown in T-cell acute lymphoblastic leukaemia 9, which leads to aberrant transcriptional activity. In addition to explaining the aberrant activation of oncogenes in leukaemic cells, highly active enhancers may create genetic instability. We explored the transcriptional features associated with structural variation sites in precursor B-cell leukaemias, which are represented by deletions, insertions, translocations and inversions. Structural variation sites tended to accumulate at certain loci in the leukaemia genomes and were more prevalent at actively transcribed regions. We discovered that simultaneous transcription from both DNA strands was often associated with these recurring structural variation sites 10. Enhancers that overlapped gene regions displayed this feature, which was accompanied by polymerase II stalling, access to DNA cleavage motifs and R-loop formation, a DNA:RNA hybrid formed during the transcription, which could lead to double-stranded DNA breaks that promote off-target cleavage by recombination-activating genes and activation-induced cytidine deaminase complexes. Stalled polymerases were found within the first intron of many key regulators of cell differentiation and tumour suppressor genes, including paired box protein 5 (PAX5) and B-cell translocation gene 1 (BTG1) protein 10, 11. The preleukaemic cells arrested at the pre-B stage are thereby vulnerable to secondary lesions at such genomic regions. Taken together, these observations highlighted that altered activity or mutations in both coding and noncoding regions can have a profound effect on the regulatory network that governs lymphoid cell differentiation. With the power of ever-developing sequencing techniques, these alterations, which may lead to childhood leukaemia, are now being elucidated at a genomewide scale. It is also tempting to speculate that the detection of changes in the regulatory elements of the genome might guide us to put patients in more specific stratifications as well as increase our understanding of the leukaemia as a disease.

Solid nanopore-based deoxyribonucleic acid (DNA) sequencing has led to low-cost, fast, reliable, controlled, and amplified or label-free and high-resolution recognition and identification of DNA nucleotides. Solid-state materials and biological nanopores have a low signal-to-noise ratio (SNR) and generally are too thick to read at single-nucleotide resolution. The issue with solid-state nanopores is that the DNA strands stick to the nanopore sides and on the surface during the translocation process. The coexistence of DNA nucleotides on the surface and the nanopore sides will complicate the ionic current signals, making nucleotide detection difficult. Therefore, different sized nanogaps can be promising to overcome some of these issues. Using all-atom molecular dynamics (MD) simulations, we have studied the translocation of single-stranded (ss) DNA through solid-state nanogaps embedded in a graphene membrane device. A nucleotide-specific DNA sequencing technique is proposed based on unique differences in the ionic current responses for all the four ssDNA nucleotides (dAMP16, dGMP16, dTMP16, and dCMP16). As the individual homogeneous ssDNA translocate through the nanogaps, characteristic changes are observed in the ionic current. Our results show that ssDNA nucleotides can translocate through the proposed graphene nanogap devices by applying an external electric field. In addition, the sticking issue can be resolved using graphene nanogaps during the ssDNA translocation processes. Therefore, the significant difference in ionic current sensitivity and the translocation event/time yielded by the graphene nanogap-based devices reveal possibilities for utilizing it for ultrafast nanogap-based DNA sequencing.

A new Meta heuristic optimization method based on Newton's laws of gravity and motion has been developed by Rashedi E. et al. known as Gravitational Search Algorithm (GSA). In this paper GSA is implemented to economic operation of a two area power system and computes how much power has to be generated internally in an area and how much power has to be borrowed from other area through tie-line for a specified load in most economical sense. This method is explained with an example and the result obtained by the proposed method is compared with by particle swarm optimization (PSO) as reported in literature. It has been shown that this method is more efficient and takes less computation time than PSO.