Search

DIA is a mainstream method for quantitative proteomics, but consistent quantification across multiple LC-MS/MS instruments remains a bottleneck in parallelizing data acquisition. One reason for this inconsistency and missing quantification is the retention time shift which current software does not adequately address for runs from multiple sites. We present multirun chromatogram alignment strategies to map peaks across columns, including the traditional reference-based Star method, and two novel approaches: MST and Progressive alignment. These reference-free strategies produce a quantitatively accurate data-matrix, even from heterogeneous multi-column studies. Progressive alignment also generates merged chromatograms from all runs which has not been previously achieved for LC-MS/MS data. First, we demonstrate the effectiveness of multirun alignment strategies on a gold-standard annotated dataset, resulting in a threefold reduction in quantitation error-rate compared to non-aligned DIA results. Subsequently, on a multi-species dataset that DIAlignR effectively controls the quantitative error rate, improves precision in protein measurements, and exhibits conservative peak alignment. We next show that the MST alignment reduces cross-site CV by 50% for highly abundant proteins when applied to a dataset from 11 different LC-MS/MS setups. Finally, the reanalysis of 949 plasma runs with multirun alignment revealed a more than 50% increase in insulin resistance (IR) and respiratory viral infection (RVI) proteins, identifying 11 and 13 proteins respectively, compared to prior analysis without it. The three strategies are implemented in our DIAlignR workflow (>2.3) and can be combined with linear, non-linear, or hybrid pairwise alignment., (© 2023. The Author(s).)

Multi-run alignment is widely used in proteomics to establish analyte correspondence across runs. Generally alignment algorithms return a cumulative score, which may not be easily interpretable for each peptide. Here a novel and interactive tool for cross-run chromatogram alignment visualization (DrawAlignR) of data-independent acquisition (DIA) data is presented. Furthermore, a novel C++ based implementation of raw chromatogram alignment which is 35 times faster than the previously published algorithm is developed. This not only enables users to plot alignment interactively by DrawAlignR, but also allows other software platforms to use the algorithm. DrawAlignR is an open-source web application using R Shiny that can be hosted using the source-code available at https://github.com/Roestlab/DrawAlignR., (© 2020 Wiley-VCH GmbH.)

Balancing the exploration and exploitation in any nature-inspired optimization algorithm is an essential task, while solving the real-world global optimization problems. Therefore, the search agents of an algorithm always try to explore the unvisited domains of a search space in a balanced manner. The sine cosine algorithm (SCA) is a recent addition to the field of metaheuristics that finds the solution of an optimization problem using the behavior of sine and cosine functions. However, in some cases, the SCA skips the true solutions and trapped at sub-optimal solutions. These problems lead to the premature convergence, which is harmful in determining the global optima. Therefore, in order to alleviate the above-mentioned issues, the present study aims to establish a comparatively better synergy between exploration and exploitation in the SCA. In this direction, firstly, the exploration ability of the SCA is improved by integrating the social and cognitive component, and secondly, the balance between exploration and exploitation is maintained through the grey wolf optimizer (GWO). The proposed algorithm is named as SC-GWO. For the performance evaluation, a well-known set of benchmark problems and engineering test problems are taken. The dimension of benchmark test problems is varied from 30 to 100 to observe the robustness of the SC-GWO on scalability of problems. In the paper, the SC-GWO is also used to determine the optimal setting for overcurrent relays. The analysis of obtained numerical results and its comparison with other metaheuristic algorithms demonstrate the superior ability of the proposed SC-GWO. [ABSTRACT FROM AUTHOR]

Grey wolf optimizer (GWO) is a recently developed population-based algorithm in the area of nature-inspired optimization. The leading hunters in GWO are responsible for exploring the new promising regions of the search space. However, in some circumstances, the classical GWO suffers from the problem of premature convergence due to the stagnation at sub-optimal solutions. The insufficient guidance of search in GWO leads to slow convergence. Therefore, to alleviate from all the above issues, an improved leadership-based GWO called GLF–GWO is introduced in the present paper. In GLF–GWO, the leaders are updated through Levy-flight search mechanism. The proposed GLF–GWO algorithm enhances the search efficiency of leading hunters in GWO and provides better guidance to accelerate the search process of GWO. In the GLF–GWO algorithm, the greedy selection is introduced to avoid their divergence from discovered promising areas of the search space. To validate the efficiency of the GLF–GWO, the standard benchmark suite IEEE CEC 2014 and IEEE CEC 2006 are taken. The proposed GLF–GWO algorithm is also employed to solve some real-engineering problems. Experimental results reveal that the proposed GLF–GWO algorithms significantly improve the performance of the classical version of GWO. [ABSTRACT FROM AUTHOR]

Abstract Grey Wolf Optimizer (GWO) algorithm is a relatively new algorithm in the field of swarm intelligence for solving continuous optimization problems as well as real world optimization problems. The Grey Wolf Optimizer is the only algorithm in the category of swam intelligence which is based on leadership hierarchy. This paper has three important aspects- Firstly, for improving the search ability by grey wolf a modified algorithm RW-GWO based on random walk has been proposed. Secondly, its performance is exhibited in comparison with GWO and state of art algorithms GSA, CS, BBO and SOS on IEEE CEC 2014 benchmark problems. A non-parametric test Wilcoxon and Performance Index Analysis has been performed to observe the impact of improving the leaders in the proposed algorithm. The results presented in this paper demonstrate that the proposed algorithm provide a better leadership to search a prey by grey wolves. The third aspect of the paper is to use the proposed algorithm and GWO on real life application problems. It is concluded from this article that RW-GWO algorithm is an efficient and reliable algorithm for solving not only continuous optimization problems but also for real life optimization problems. [ABSTRACT FROM AUTHOR]

The sine–cosine algorithm (SCA) is one of the simplest and efficient stochastic search algorithms in the field of metaheuristics. It has shown its efficacy in solving several real-life applications. However, in some cases, it shows stagnation at local optima and premature convergence issues due to low exploitation ability and insufficient diversity skills. To overcome these issues from the SCA, its enhanced version named ISCA is developed in this paper. The proposed ISCA is designed based on modifying the original search mechanism of the SCA and hybridizing it with a differential evolution (DE) algorithm. The search procedure in the ISCA switches between the modified search mechanism of the SCA and DE based on the evolutionary states of candidate solutions and a parameter called the switch parameter. The modified SCA enhances the exploitation ability and convergence speed, while the DE maintains the diversity of the population to avoid local optimal solutions. The parameters of the ISCA are tuned in such as way that they could balance the exploration and exploitation features. Validation of the ISCA is conducted on a benchmark set of 23 continuous optimization problems through different performance measures, which reveals its effectiveness as a better optimizer for continuous optimization problems. Furthermore, the proposed ISCA is extended to develop its efficient binary version named BISCA for solving multidimensional knapsack problems. A benchmark collection of 49 instances is used for the performance evaluation of the BISCA. Comparison of results produced by the BISCA with other algorithms and previous studies indicates its better search efficiency and verifies it as an effective alternative for solving the MKP. • Enhanced version of the sine–cosine algorithm (SCA) named ISCA is developed. • ISCA is designed to balance exploitation and exploration during its search procedure. • Validation of ISCA is carried out on benchmark optimization problems. • ISCA is used to solve NP-hard multidimensional knapsack problems. • Comparison of performance indicates the efficacy of the ISCA. [ABSTRACT FROM AUTHOR]

Sine cosine algorithm (SCA) is a recently developed and widely used metaheuristic to perform global optimization tasks. Due to its simplicity in structure and reasonable performance, it has been utilized to solve several real-world applications. This paper proposes an alternate version of the SCA by adopting the greedy approach of search, crossover and exponentially decreased transition control parameter to overcome the issues of low exploitation, insufficient diversity and premature convergence. The proposed algorithm, called ECr-SCA, is validated and compared with the original SCA using computational time, diversity, performance index, statistical and convergence analysis on a set of 23 standard benchmark problems. Later, the proposed ECr-SCA is compared with seventeen other algorithms including improved versions of the SCA and state-of-the-art algorithms. Furthermore, the ECr-SCA is used to train multi-layer perceptron and the results are compared with variants of SCA and other metaheuristics. Overall comparison based on several different metrics illustrates the significant improvement in the search strategy of the SCA by the proposal of the ECr-SCA. • The enhanced sine cosine algorithm (ECr-SCA) is proposed for global optimization. • A greedy search and modified transition parameter are used to increase exploitation. • The multi-parent crossover is embedded to enhance diversity and global search. • The excellent performance of the ECr-SCA is validated on benchmark problems. • The ECr-SCA is also used to train multilayer perceptron. [ABSTRACT FROM AUTHOR]

This study addresses the traffic light scheduling problem for pedestrian–vehicle mixed-flow networks. A macroscopic model, which strikes an appropriate balance between pedestrians' needs and vehicle drivers' needs, is employed to describe the traffic light scheduling problem in a scheduling framework. The objective of this problem is to minimize the total network-wise delay time of vehicles and pedestrians within a given finite-time window, which is crucial to avoid traffic congestion in urban road networks. To achieve this objective, the present study first uses a well-known optimization solver called GUROBI to obtain the optimal solution by converting the problem into mixed-integer linear programming. The obtained results indicate the computational inefficiency of the solver for large network sizes. To overcome this computational inefficiency, three novel metaheuristic methods based on the sine–cosine algorithm are proposed. These methods are denoted by discrete sine–cosine algorithm, discrete sine–cosine algorithm with local search operator, and discrete sine–cosine algorithm with local search operator and memory utilization inspired by harmony search. Each of these methods is developed hierarchically by taking the advantages of previously developed method(s) in terms of a better search process to provide more accurate solutions and a better convergence rate. To validate all these proposed metaheuristics, extensive computational experiments are carried out using the real traffic infrastructure of Singapore. Moreover, various performance measures such as statistical optimization results, relative percentage deviation, computational time, statistical analysis, and convergence behavior analysis have been employed to evaluate the performance of algorithms. The comparison of the proposed SCA variants is done with GUROBI solver and other metaheuristics namely, harmony search, firefly algorithm, bat algorithm, artificial bee colony, genetic algorithm, salp swarm algorithm, and harris hawks optimization. Overall comparison analysis concludes that the proposed methods are very efficient to solve the traffic light scheduling problem for pedestrian–vehicle mixed-flow networks with different network sizes and prediction time horizons. • A macroscopic model for urban traffic light scheduling problem (PV-TLSP). • PV-TLSP considers pedestrian–vehicle mixed-flow networks. • Discrete sine–cosine algorithm is proposed for solving PV-TLSP. • Enhanced DSCA variants are proposed using local search and memory utilization. • Extensive experiments are carried out to verify the efficacy of proposed algorithms. [ABSTRACT FROM AUTHOR]

Uncertainties in design variables and problem parameters are often inevitable and must be considered an optimization task if reliable optimal solutions are sought. Besides a number of sampling techniques, there exist several mathematical approximations of a solution's reliability. These techniques are coupled in various ways with optimization the classical reliability-based optimization field. This paper demonstrates how classical reliability-based concepts can be borrowed and modified and, with integrated single and multiobjective evolutionary algorithms, used to enhance their scope in handling uncertainties involved among decision variables and problem parameters. Three different optimization tasks are discussed in which classical reliability-based optimization procedures usually have difficulties, namely 1) reliability-based optimization problems having multiple local optima, finding and revealing reliable solutions for different reliability indices simultaneously by means of a bi-criterion optimization approach, and 3) multiobjective optimization with uncertainty and specified system or component reliability values. Each these optimization tasks is illustrated by solving a number of test problems and a well-studied automobile design problem. Results are also compared with a classical reliability-based methodology. [ABSTRACT FROM AUTHOR]

• Nine recent meta-heuristics are used to optimize eight mechanical design problems. • Theoretical and numerical comparisons are extensively investigated. • The results show the merits of the methods used in solving the case studies. Determining the solution for real mechanical design problems is a challenging task when using the newly developed and efficient swarm intelligence algorithms. There are so many difficulties to be addressed, including but not limited to mixed decision variables, diverse constraints, inherent errors, conflicting objectives, and numerous locally optimal solutions. This work analyzes the behavior of nine metaheuristic algorithms, namely, salp swarm algorithm (SSA), multi-verse optimizer (MVO), moth-flame optimizer (MFO), atom search optimization (ASO), ecogeography-based optimization (EBO), queuing search algorithm (QSA), equilibrium optimizer (EO), evolutionary strategy (ES) and hybrid self-adaptive orthogonal genetic algorithm (HSOGA). The efficiency of these algorithms is evaluated on eight mechanical design problems using the solution quality and convergence analysis, which verifies the wide applicability of these algorithms to real-world application problems. [ABSTRACT FROM AUTHOR]

• A new method to solve global optimization and engineering problems called m-HHO. • The m-HHO improves the HHO using new transition rule and opposition-based learning. • A collection of 33 benchmarks is taken to evaluate the performance. • The m-HHO is also tested on engineering optimization problems. • Comparisons illustrate the improvement on the performance of m-HHO. Harris hawks optimizer (HHO) is a recently developed, efficient meta-heuristic optimization approach, which is inspired by the chasing style and collaborative behavior of Harris hawks in nature. However, for some optimization cases, the algorithm suffers from an immature balance between exploitation and exploration. Therefore, in the present study, four effective strategies are introduced into conventional HHO, such as proposing a non-linear energy parameter for the nergy of prey, differor rapid dives, a greedy selection mechanism, and opposition-based learning. These strategies enhance the search-efficiency of HHO and help to alleviate the issues of stagnation at the sub-optimal solution and premature convergence. A well-known collection of 33 benchmark problems is taken to examine the effectiveness of the proposed m-HHO, and the comparison is performed with conventional HHO and other state-of-the-art algorithms. Accordingly, the proposed m-HHO can serve as an effective and efficient optimization tool for global optimization problems. [ABSTRACT FROM AUTHOR]

To alleviate the shortcomings of the standard Equilibrium Optimizer, a new improved algorithm called Modified Equilibrium Optimizer is proposed in this work. This algorithm utilizes the Gaussian mutation and an additional exploratory search mechanism based on the concept of population division and reconstruction. The population in each iteration of the proposed algorithm is constructed using these mechanisms and standard search procedure of the Equilibrium Optimizer. These strategies attempt to maintain the diversity of solutions during the search, so that the tendency of stagnation towards the sub-optimal solutions can be avoided and the convergence rate can be boosted to obtain more accurate optimal solutions. To validate and analyze the performance of the Modified Equilibrium Optimizer, a collection of 33 benchmark problems and four engineering design problems are adopted. Later, in the paper, the Modified Equilibrium Optimizer has been used to train multilayer perceptrons. The experimental results and comparison based on several metrics such as statistical analysis, scalability test, diversity analysis, performance index analysis and convergence analysis demonstrate that the proposed algorithm can be considered a better metaheuristic optimization approach than other compared algorithms. • A new method has been proposed, known as m-EO, for global optimization. • The m-EO is developed using population division, mutation and novel exploration strategy. • A set of 33 benchmark problems is used to examine the m-EO. • The m-EO is used to solve engineering design problems and to train multilayer perceptron. • Comparisons illustrate the improvement in the performance of the m-EO. [ABSTRACT FROM AUTHOR]

• A new method has been proposed, known as MSCA, for global optimization. • The MSCA improves the SCA using a novel transition parameter and mutation operator. • A set of 33 benchmark problems is used to examine the MSCA. • The MSCA is also used to solve real-engineering problems and to train multilayer perceptron. • Comparisons illustrate the improvement in the performance of the MSCA. Inspired by the mathematical characteristics of sine and cosine trigonometric functions, the Sine Cosine Algorithm (SCA) has shown competitive performance among other meta-heuristic algorithms. However, despite its sufficient global search ability, its low exploitation ability and immature balance between exploitation and exploration remain weaknesses. In order to improve Sine Cosine Algorithm (SCA), this paper presents a modified version of the SCA called MSCA. Firstly, a non-linear transition rule is introduced instead of a linear transition to provide comparatively better transition from the exploration to exploitation. Secondly, the classical search equation of the SCA is modified by introducing the leading guidance based on the elite candidate solution. When the above proposed modified search mechanism fails to provide a better solution, in addition, a mutation operator is used to generate a new position to avoid the situation of getting trapped in locally optimal solutions during the search. Thus, the MSCA effectively maximizes the advantages of proposed strategies in maintaining a comparatively better balance of exploration and exploitation as compared to the classical SCA. The validity of the MSCA is tested on a set of 33 benchmark optimization problems and employed for training multilayer perceptrons. The numerical results and comparisons among several algorithms show the enhanced search efficiency of the MSCA. [ABSTRACT FROM AUTHOR]

Generation of the hot spot in photovoltaic cells (PV) cells under partial shading conditions (PSC) is a persistent issue associated with PV systems which not only affects the PV performance but also increases the cell temperature (up to 400 °C in extreme cases), and thereby causes premature failure. This article presents a novel technique to prevent the reverse breakdown and thereby mitigate the hot spot temperature of the PV cells under partial shading by reducing reverse current through automated reconfiguration of PV array. The efficacy of the proposed technique is evaluated by simulating eight different shading scenarios, under which the reconfiguration algorithm automatically switched different PV configurations based on the nature of the shading pattern. The performance of the PV arrays is evaluated using IGBT as the switching device, and the performance is compared with an ideal switch-based circuit. The proposed technique is found to reduce the reverse voltage and reverse current and through the affected modules and thereby increase the reliability index in 82% of the cases. Hence the proposed reconfiguration algorithm is expected to increase the PV reliability and thereby increase the performance ratio of the PV projects, especially which are susceptible to frequent partial shading conditions. • Delineates the advantages and disadvantages associated with PV energy. • A novel technique is presented for alleviating the reliability issues. • The proposed reconfiguration technique reduced the reverse current substantially. • The detailed algorithm of the proposed may be integrated with the MPPT algorithm. • Reliability Index is calculated for the proposed technique to quantify its advantages. [ABSTRACT FROM AUTHOR]