Worst Solution Research Articles

DJAYA: A discrete Jaya algorithm for solving traveling salesman problem

Jaya algorithm is a newly proposed stochastic population-based metaheuristic optimization algorithm to solve constrained and unconstrained continuous optimization problems. The main difference of this algorithm from the similar approaches, it uses best and worst solution in the population in order improve the intensification and diversification of the population, and this provides discovering potential solutions on the search space of the optimization problem. In this study, we propose discrete versions of the Jaya by using two major modifications in the algorithm. First is to generate initial solutions by using random permutations and nearest neighborhood approach to create population. Second is the update rule of the basic Jaya algorithm rearranged to solve discrete optimization problems. Due to characteristics of the discrete optimization problem, eight transformation operators are used for the discrete variants of the proposed algorithm. Based on these modifications, the discrete Jaya algorithm, called DJAYA, has been applied to solve fourteen different symmetric traveling salesman problem, which is one of the famous discrete problems in the discrete optimization. In order to improve the obtained best solution from DJAYA, 2-opt heuristic is also applied to the best solution of DJAYA. Once population size, search tendency and the other parameters of the proposed algorithm have been analyzed, it has been compared with the state-of-art algorithms and their variants, such as Simulated Annealing (SA), Tree-Seed Algorithm (TSA), State Transition Algorithm (STA) Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), Genetic Algorithm (GA) and Black Hole (BH). The experimental results and comparisons show that the proposed DJAYA is highly competitive and robust optimizer for the problem dealt with the study.

Feature Selection Using Enhanced Particle Swarm Optimisation for Classification Models.

In this research, we propose two Particle Swarm Optimisation (PSO) variants to undertake feature selection tasks. The aim is to overcome two major shortcomings of the original PSO model, i.e., premature convergence and weak exploitation around the near optimal solutions. The first proposed PSO variant incorporates four key operations, including a modified PSO operation with rectified personal and global best signals, spiral search based local exploitation, Gaussian distribution-based swarm leader enhancement, and mirroring and mutation operations for worst solution improvement. The second proposed PSO model enhances the first one through four new strategies, i.e., an adaptive exemplar breeding mechanism incorporating multiple optimal signals, nonlinear function oriented search coefficients, exponential and scattering schemes for swarm leader, and worst solution enhancement, respectively. In comparison with a set of 15 classical and advanced search methods, the proposed models illustrate statistical superiority for discriminative feature selection for a total of 13 data sets.

Parallel Algorithm with Blocks for a Single-Machine Total Weighted Tardiness Scheduling Problem

In this paper, the weighted tardiness single-machine scheduling problem is considered. To solve it an approximate (tabu search) algorithm, which works by improving the current solution by searching the neighborhood, is used. Methods of eliminating bad solutions from the neighborhood (the so-called block elimination properties) were also presented and implemented in the algorithm. Blocks allow a significant shortening of the process of searching the neighborhood generated by insert type moves. The designed parallel tabu search algorithm was implemented using the MPI (Message Passing Interface) library. The obtained speedups are very large (over 60,000×) and superlinear. This may be a sign that the parallel algorithm is superior to the sequential one as the sequential algorithm is not able to effectively search the solution space for the problem under consideration. Only the introduction of diversification process through parallelization can provide an adequate coverage of the entire search process. The current methods of parallelization of metaheuristics give a speedup which strongly depends on the problem’s instances, rarely greater than number of used parallel processors. The method proposed here allows the obtaining of huge speedup values (over 60,000×), but only when so-called blocks are used. The above-mentioned speedup values can be obtained on high performance computing infrastructures such as clusters with the use of MPI library.

Enhancement of travel range of electrostatically driven fixed-fixed microbeam in static and dynamic mode using mutated particle swarm optimization

Particle swarm optimization (PSO) is a substantially exercised technique for optimizing structural design problems. One major snag of particle swarm optimization is a possibility of premature convergence, observed in the specific cases where global optimum is encircled by the solutions having higher objective function values. These territories are unexplored and swarm can fall into local optimum. The present work proposes to suggest one of the variants of the PSO algorithm to enhance the reliability. In the proposed algorithm, during each iteration, few particles will be chosen to follow worst solution, this would enable to perform exploration and exploitation of design space simultaneously. Hence, the probability of not getting caught into local optima gets augmented which gives better assurance to the achieved solution. The mutated particle swarm optimization (MPSO) is efficaciously employed for shape optimization of electrostatically driven fixed-fixed microbeams to augment the static and dynamic travel range. The geometry of fixed-fixed microbeams is optimized using continuous, smooth, parametric functions of width and thickness. For structural analysis, Rayleigh–Ritz energy method is utilized to compute the static and dynamic pull-in parameters. Nonlinear design constraints of engineering interest are implemented through penalty approach. Using modified particle swarm optimization, width and thickness profiles of fixed-fixed microbeams that result in maximum pull-in displacement have been obtained. Augmented pull-in parameters for optimized geometry of microbeams have been endorsed using 3-D finite element analysis. The optimization parameters reveal maximum rise in the static pull-in displacement by 41.25% and dynamic pull-in displacement by 39.15% of fixed-fixed microbeam compared to referential prismatic microbeam. Efficacy of mutated particle swarm optimization is emphasized through 18 representative cases. Mutated particle swarm optimization converges in around 20% of time in comparison with the hybrid simulated annealing, without getting trapped into local minimum in all the test cases.

Classification of Parkinson disease using binary Rao optimization algorithms

AbstractRao algorithms are recently proposed optimization algorithms used to solve optimization problems. These algorithms are based on the best and the worst solutions, which are computed during the optimization process. However, these algorithms apply to continuous problems only. In this article, the binary versions of Rao algorithms are proposed, which can be used for solving feature selection problems. These are applied to four publicly available Parkinson's disease datasets. Besides providing an optimal set of features, the k parameter of the k‐nearest neighbour classifier is also optimized by the proposed approach. The performance of these algorithms has been measured taking an average of 30 independent runs using a 10‐fold cross‐validation procedure. Also, a comparison of the performance has been made with the other state of the art methods. Significance analysis of these algorithms has been made with the Friedman rank test.

Enhanced chaotic JAYA algorithm for parameter estimation of photovoltaic cell/modules

Parameters for defining photovoltaic models using measured voltage–current​ characteristics are essential for simulation, control, and evaluation of photovoltaic-based systems. This paper proposes an enhanced chaotic JAYA algorithm to classify the parameters of various photovoltaic models, such as the single-diode and double-diode models, accurately and reliably. The proposed algorithm introduces a self-adaptive weight to regulate the trend to reach the optimal solution and avoid the worst solution in various phases of the search space. The self-adaptive weight capability also allows the proposed technique to reach the best solution at the earliest phase, and later, the local search process starts, which also increase the ability to explore. A three different chaotic process, including sine, logistics and tent map, is proposed to optimize the consistency of each generation’s best solution. The proposed algorithm and its variants proposed are used to solve the parameter estimation problem of various PV models. To show the proficiency of the suggested algorithm and its variants, an extensive simulation is carried out using MATLAB/Simulink software. Two statistical tests are conducted and compared with the latest techniques for validating the performance of the suggested algorithm and its variants. Comprehensive analysis and experimental results display that the suggested algorithm can achieve highly competitive efficiency in terms of accuracy and reliability compared to other algorithms in the literature. This research will be backed up with extra online service and guidance for the paper’s source code at https://premkumarmanoharan.wixsite.com/mysite.

An Evolutionary Algorithm with Clustering-Based Assisted Selection Strategy for Multimodal Multiobjective Optimization

In multimodal multiobjective optimization problems (MMOPs), multiple Pareto optimal sets, even some good local Pareto optimal sets, should be reserved, which can provide more choices for decision-makers. To solve MMOPs, this paper proposes an evolutionary algorithm with clustering-based assisted selection strategy for multimodal multiobjective optimization, in which the addition operator and deletion operator are proposed to comprehensively consider the diversity in both decision and objective spaces. Specifically, in decision space, the union population is partitioned into multiple clusters by using a density-based clustering method, aiming to assist the addition operator to strengthen the population diversity. Then, a number of weight vectors are adopted to divide population into N subregions in objective space (N is population size). Moreover, in the deletion operator, the solutions in the most crowded subregion are first collected into previous clusters, and then the worst solution in the most crowded cluster is deleted until there are N solutions left. Our algorithm is compared with other multimodal multiobjective evolutionary algorithms on the well-known benchmark MMOPs. Numerical experiments report the effectiveness and advantages of our proposed algorithm.

Modified whale optimisation-based optimal location of UPQC in distribution system for power quality improvement

A number of researchers has studied the unified power quality conditioner (UPQC) as an eventual solution to enhance the power quality in the electrical distribution system. However, due to the high cost, the location or position of UPQC in distribution system needs to be decided with immense care and must preferably be solved as the optimisation problem. This paper presents a power quality improvement model based on a new modified optimisation algorithm, namely worst solution linked whale optimisation algorithm update (WS-WU). The proposed algorithm finds the optimal location of the UPQC device concerning the power system losses, UPQC cost, and voltage stability index as well. The experimentation is carried out in both IEEE 33 and 69 bus systems. The performance of the proposed model is compared over other conventional methods in terms of performance and convergence analysis, and proves the superiority of the proposed method.

An effective cooperative aligner to resolve multiple-sequence alignment problem

In this research work, we propose a new cooperative aligner based on metaheuristics to find an approximate solution to the multiple-sequence alignment (MSA) problem. The developed approach named HPSOSA apply in the first stage the particle swarm optimisation (PSO) with a crossover operator as a move mechanism for each particle. In the second stage, simulated annealing is incorporated in order to improve worst solutions in the population and to help HPSOSA to escape from local optimum alignment. Simulation results on BaliBASE benchmarks have demonstrated the capability of the proposed method to obtain better results for the MSA problem compared to those produced by some literature works in the same field.

Sensory evaluation of apple ber using fuzzy TOPSIS

Abstract The products produced using Apple Ber powder needs to be evaluated for its quality. Quality of the product has to be evaluated carefully in order to improve the process and enhance the customer satisfaction. Sensory evaluation plays a significant role in assessing the quality of the product. Nine (09) point scale is generally used for collecting opinions from the expert regarding various parameters which showcases the quality of the product. This opinion is evaluated and ranked using Fuzzy TOPSIS. Ranking is done based on the distance from the ideal best and ideal worst solutions. This ranking will be useful for the producers to choose the right blend of ingredients and improve the overall acceptability of the product by the customers.

An improved Henry gas solubility optimization algorithm for task scheduling in cloud computing

In cloud computing, task scheduling plays a major role and the efficient schedule of tasks can increase the cloud system efficiency. To successfully meet the dynamic requirements of end-users’ applications, advanced scheduling techniques should be in place to ensure optimal mapping of tasks to cloud resources. In this paper, a modified Henry gas solubility optimization (HGSO) is presented which is based on the whale optimization algorithm (WOA) and a comprehensive opposition-based learning (COBL) for optimum task scheduling. The proposed method is named HGSWC. In the proposed HGSWC, WOA is utilized as a local search procedure in order to improve the quality of solutions, whereas COBL is employed to improve the worst solutions by computing their opposite solutions and then selecting the best among them. HGSWC is validated on a set of thirty-six optimization benchmark functions, and it is contrasted with conventional HGSO and WOA. The proposed HGSWC has been proved to perform better than the comparison algorithms. Moreover, the performance of HGSWC has also been tested on a set of synthetic and real workloads including fifteen different task scheduling problems. The results obtained through simulation experiments demonstrate that HGSWC finds near optimal solutions with no computational overhead as well as outperforms six well-known metaheuristic algorithms.

Momentum search algorithm: a new meta-heuristic optimization algorithm inspired by momentum conservation law

A novel optimization methodology, Momentum Search Algorithm (MSA) is presented based on Newton’s laws: the law of conservation of momentum. It includes a set of masses in a closed system considering the conservation of momentum and kinetic energy of bodies. The possible solutions are presented by system bodies’ positions in an n-dimensional space. The mass of bodies is proportional to their fitness function. Larger masses represent the better solutions. At each iteration, an external body collides separately with all solution bodies and moves them toward the optimum solution. The direction of the collision depends on the position of solution bodies and the position of the body with the best fitness function. As the better solutions have heavier bodies, the external body has less effect on their positions. On the other hand, the worse solutions are lighter and moved easily by the external body toward the better positions. The best position is achieved by allowing the external body to move the solution bodies toward better positions. The numerical results obtained from several standard benchmark test functions indicate the superiority of the proposed method over many other optimization techniques such as Genetic Algorithm, Particle Swarm Optimization, Gravitational Search Algorithm, Teaching–Learning-Based Optimization, Grey Wolf Optimizer, Grasshopper Optimization Algorithm, Spotted Hyena Optimizer, and Emperor Penguin Optimizer.

Differential evolution algorithm with multi-population cooperation and multi-strategy integration

Differential evolution with a multi-population based ensemble of mutation strategies (MPEDE) has been considered among the most efficient Evolutionary Algorithms for global optimization. Our research results reveal that the performance of MPEDE may be improved by adding an information sharing mechanism and modifying the grouping mechanism. In MPEDE, the entire population is divided into four subpopulations, and most computing resources are allocated to the best strategy, but a better strategy has the same computing resources as the worst strategy. In order to rationally distribute computational resources, a differential evolution variant with multi-population cooperation and multi-strategy integration (MPMSDE) is proposed in this paper. MPMSDE develops a new grouping method instead of the grouping method in MPEDE, and the new grouping method utilizes the ranking of strategies to assign computational resources to different strategies. Also, an information sharing mechanism is introduced in the largest sub-population to avoid falling into local optimum. In MPMSDE, a new mutation strategy, “DE/pbad-to-pbest-to-gbest/1”, is used to replace the mutation strategy “DE/rand/1” in MPEDE. The new strategy not only uses personal history optimal solution and the worst solution but also uses the global best solution to update individuals. The new strategy can not only balance exploration and exploitation but also can accelerate the convergence of the algorithm. The performance of MPMSDE is compared with MPEDE and other state-of-the-art evolutionary algorithms on CEC2005 and CEC2014 benchmark functions. The experimental results show that the performance of the MPMSDE algorithm is very competitive in calculation accuracy and convergence speed.

Assessment of performance of telecom service providers using intuitionistic fuzzy grey relational analysis framework (IF-GRA)

Over two decades, telecommunication market has been continuing a huge growth in urban, semi-urban and rural regions of India. Investigating and choosing the desirable cellular mobile telephone service providers (TSPs) based on operative constraint can facilitate to obtain the ideal of TSPs selection. Here, the evaluation commonly encompasses different TSP options under various operational factors; thus, the assessment of TSPs can be considered as a rigorous multi-criteria decision-making (MCDM) issue. This paper initiates a structure for the exploration of the TSPs in the Madhya Pradesh region, India. To do this, a framework associated with grey relational analysis (GRA) on intuitionistic fuzzy sets (IFSs) is planned to obtain the performance of various telecom participants. The developed approach is based on the conception of the best and worst solutions. To find the attribute weights, a divergence measure is developed and employed by relative comparisons. Additionally, to show the proficiency and practicality, a selection problem of TSPs options of Madhya Pradesh circle, India is presented within the IFSs context. By employing the developed model, an expert can enlarge tactics to enhance the performance by standard operational factors. Comparison and sensitivity analysis are considered to validate the developed approach in the prioritization of the TSPs options.

Representation of the Denmark Strait overflow in a <i>z</i>-coordinate eddying configuration of the NEMO (v3.6) ocean model: resolution and parameter impacts

Abstract. We investigate in this paper the sensitivity of the representation of the Denmark Strait overflow produced by a regional z-coordinate configuration of NEMO (version 3.6) to the horizontal and vertical grid resolutions and to various numerical and physical parameters. Three different horizontal resolutions, 1∕12, 1∕36, and 1/60∘, are respectively used with 46, 75, 150, and 300 vertical levels. In the given numerical set-up, the increase in the vertical resolution did not bring improvement at eddy-permitting resolution (1/12∘). We find a greater dilution of the overflow as the number of vertical level increases, and the worst solution is the one with 300 vertical levels. It is found that when the local slope of the grid is weaker than the slope of the topography the result is a more diluted vein. Such a grid enhances the dilution of the plume in the ambient fluid and produces its thickening. Although the greater number of levels allows for a better resolution of the ageostrophic Ekman flow in the bottom layer, the final result also depends on how the local grid slope matches the topographic slope. We also find that for a fixed number of levels, the representation of the overflow is improved when horizontal resolution is increased to 1∕36 and 1/60∘, with the most drastic improvements being obtained with 150 levels. With such a number of vertical levels, the enhanced vertical mixing associated with the step-like representation of the topography remains limited to a thin bottom layer representing a minor portion of the overflow. Two major additional players contribute to the sinking of the overflow: the breaking of the overflow into boluses of dense water which contribute to spreading the overflow waters along the Greenland shelf and within the Irminger Basin, and the resolved vertical shear that results from the resolution of the bottom Ekman boundary layer dynamics. This improves the accuracy of the calculation of the entrainment by the turbulent kinetic energy mixing scheme (as it depends on the local shear) and improves the properties of the overflow waters such that they more favourably compare with observations. At 300 vertical levels the dilution is again increased for all horizontal resolutions. The impact on the overflow representation of many other numerical parameters was tested (momentum advection scheme, lateral friction, bottom boundary layer parameterization, closure parameterization, etc.), but none had a significant impact on the overflow representation.

A data-driven evolutionary algorithm for wind farm layout optimization

The wind farm layout model is to optimize the location of wind turbines to maximize the power output of the wind farm. Due to the complexity of the wind farm layout problem, the computation of objective function costs lots of time. To reduce the high computational cost while maintaining the solution performance, a data-driven evolutionary algorithm is proposed. An adaptive differential evolution algorithm (ADE) is proposed as the solver of the wind farm layout model. The adaption mechanism of ADE benefits the automatic adjustment of parameters in the mutation and crossover operators to achieve the optimal solution. The general regression neural network (GRNN) algorithm builds the data-driven surrogate model. The data-driven surrogate model is trained and updated using the data generated by the evolutionary algorithm throughout the evolution process. Through the data-driven surrogate model, the objective function is fast approximated and the bad candidate solutions are identified. The algorithm efficiency is greatly improved by fast filtering the bad candidate solutions. The ADE-GRNN is compared to other three conventional optimization methods based on two different wind scenarios. The results show the super-performance of ADE-GRNN in complex situations in terms of power output and execution time.

Parameter identification of solar cells and fuel cell using improved social spider algorithm

PurposeTo design, control and evaluate photovoltaic (PV) systems, an accurate model is required. Accuracy of PV models depends on model parameters. This study aims to use a new algorithm called improved social spider algorithm (ISSA) to detect model parameters.Design/methodology/approachTo improve performance of social spider algorithm (SSA), an elimination period is added. In addition, at the beginning of each period, a certain number of the worst solutions are replaced by new solutions in the search space. This allows the particles to find new paths to get the best solution.FindingsIn this paper, ISSA is used to estimate parameters of single-diode and double-diode models. In addition, effect of irradiation and temperature on I–V curves of PV modules is studied. For this purpose, two different modules called multi-crystalline (KC200GT) module and polycrystalline (SW255) are used. It should be noted that to challenge the performance of the proposed algorithm, it has been used to identify the parameters of a type of widely used module of fuel cell called proton exchange membrane fuel cell. Finally, comparing and analyzing of ISSA results with other similar methods shows the superiority of the presented method.Originality/valueChanges in the spider’s movement process in the SSA toward the desired response have improved the algorithm’s performance. Higher accuracy and convergence rate, skipping local minimums, global search ability and search in a limited space can be mentioned as some advantages of this modified method compared to classic SSA.

Detailed Design Optimization of Three-Fluid Parallel-Flow Plate-Fin Heat Exchanger Using Second Law Analysis

Abstract Compact parallel-flow three-fluid heat exchanger (TFHX) of plate-fin type is optimized for attaining the minimum entropy generation unit. Four different types of plate-fins (plain rectangular, offset strip, corrugated louvered, and wavy fin (WF)) which are embodied within heat exchanger have been selected for the study under both cocurrent and countercurrent flow arrangements. The range of the decision variables are 0.3≤ma, mb, mc≤3, 0.1≤Ø≤0.95, 0.1≤Lx, Ly≤2, 0.015≤H≤0.033, 100≤n≤700, 0.0001≤tf≤0.0002, 0.001≤l≤0.009, 0.01≤Lwav≤0.09, and 0.001≤A≤0.005. Genetic algorithm is selected as an optimization tool that is apt in handling various continuous and discrete variables and the problems with the complexities in the objective function as well as in constraints. Validation of the optimization model is carried out by comparing the optimum results with that obtained from the experiments, particle swarm optimization (PSO) (under without heat duty constraint), and graphical method (under with heat duty constraint). It is observed that for a specified heat duty (180 kW) and given operating conditions, corrugated louvered fin (CLF) with countercurrent flow arrangement offers the minimum entropy generation among all. It is riveting to learn that the optimum results occur for most of the considered cases when the flowrate of the central hot fluid is lower than that of the adjacent fluids. At fixed Re = 3000 for all the fin types, off-set strip fin is the most favorable one, and plain rectangular fin (PRF) offers the worst solution comparatively.

A surrogate-based optimization method for the issuance of passenger evacuation orders under ship fires

In order to reduce assembly time and improve the computational efficiency under ship fires, a surrogate-based optimization technique is proposed to estimate the optimal time of the issuance of evacuation orders, which determines the passenger response times. The Legendre polynomial chaos expansion technique is employed to construct the surrogate model of passenger assembly time with response time parameters, and validated with the original computer evacuation model. The genetic algorithm (GA) is subsequently applied to find the optimal response time combination. A 3-storey passenger ship is presented and four different fire scenarios that involve typical fire locations are considered to address the validity of this proposed method. The results suggest that this proposed method can be used to determine the optimal and the worst response time parameters with a significantly reduced number of evaluation samples. The relative error of assembly time from the surrogate model follows the normal distribution. For evacuation zone with the longest average travel distance, passengers should be informed of the immediate evacuation. For evacuation zones with short average travel distances, the optimal response time may be larger than the worst solution. This work provides a new method to make a reasonable evacuation planning in ship fires.

Sinüs Kosinüs Algoritmasının Gezgin Satıcı Problemi Üzerinde Parametre Analizi

Sine Cosine Algorithm (SCA) is a fairly new algorithm developed in 2016 by Mirjalili, likewise Black Hole Algorithm (BHA), Whale Optimization Algorithm (WOA), Artificial Atom Algorithm (A3) and Physarum-Energy Optimization Algorithm (PEO) proposed in 2013, 2016, 2018 and 2019, respectively. Due to new ideas in SCA, a few of publications have been published on SCA. SCA was applied on continuous and discrete optimization problems. In addition, there exist remarkable implementations of SCA in the field of engineering, science and technology. In this work, a parameters analysis of SCA has been done on a classical TSP (Berlin52-CTSP) and randomly generated TSP (RTSP). In order to do parameters analysis, major parameters have been changed gradually. For classical TSP, symmetric data has been taken from TSPLIB (TSP Library in net). The results are given as best, mean, worst solutions, std. deviation and CPU time for CTSP and RTSP. Besides, figures and tables demonstrate the effect of parameters for solving TSP. After adequate experimentation, based on trial-and-error methodology, optimal parameters and best ever solutions have been found. As a result, the findings indicate that major parameters of SCA influence the performance of that algorithm significantly.