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Bat Algorithm (BA) is a nature-inspired metaheuristic search algorithm designed to efficiently explore complex problem spaces and find near-optimal solutions. The algorithm is inspired by the echolocation behavior of bats, which acts as a signal system to estimate the distance and hunt prey. Although the BA has proven effective for various optimization problems, it exhibits limited exploration ability and susceptibility to local optima. The algorithm updates velocities and positions based on the current global best solution, causing all agents to converge toward a specific location, potentially leading to local optima issues in optimization problems. On this premise, this paper proposes the Modified Bat Algorithm (MBA) as an enhancement to address the local optima limitation observed in the original BA. MBA incorporates the frequency and velocity of the current best solution, enhancing convergence speed to the optimal solution and preventing local optima entrapment. While the original BA faces diversity issues, both the original BA and MBA are introduced. To assess MBA’s performance, three sets of test functions (classical benchmark functions, CEC2005, and CEC2019) are employed, with results compared to those of the original BA, Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Dragonfly Algorithm (DA). The outcomes demonstrate the MBA’s significant superiority over other algorithms. In addition, MBA successfully addresses a real-world assignment problem (call center problem), traditionally solved using linear programming methods, with satisfactory results. [ABSTRACT FROM AUTHOR]

One of the efficient tools to handle segregation of imbalanced data is support vector data description (SVDD). In contrast to support vector machine (SVM), enclosing target data in a hyper-sphere by SVDD leads to avoid biasing toward major data. SVDD can gain the best description of data when its free parameters are set to proper values. In this paper, we propose belief-based chaotic krill herd algorithm for SVDD (BCKH-SVDD) with the aim of designing effective description of data. First, we introduce a new SVDD based on belief function theory, and then, we tune the free parameters by chaotic krill herd algorithm. Belief function theory is one of the best methods to enhance decision making for uncertain data. By adding a new belief-based weight, we can decide better about the data around the SVDD boundary and the classification will be more precise. Chaotic krill herd optimization algorithm introduces chaotic maps in the krill herd algorithm. With the help of chaotic maps, the two issues, namely local optima avoidance and convergence speed, can be overcome. Thus, chaotic krill herd algorithm is constructed based on chaotic functions and automatic switching between global and local searches of krill herd. To present the power of BCKH-SVDD, several experiments have been conducted based on tenfold cross-validation over real-world data sets from UCI repository. Experimental results show the superiority of the proposed algorithm to state-of-the-art methods in terms of classification accuracy, precision and recall measures. [ABSTRACT FROM AUTHOR]

Since the conventional multilevel thresholding approaches exhaustively search the optimal thresholds to optimize objective functions, they are computational expensive. In this paper, the modified particle swarm optimization (MPSO) algorithm is proposed to overcome this drawback. The MPSO employs two new strategies to improve the performance of original particle swarm optimization (PSO), which are named adaptive inertia (AI) and adaptive population (AP), respectively. With the help of AI strategy, inertia weight is variable with the searching state, which helps MPSO to increase search efficiency and convergence speed. Moreover, with the help of AP strategy, the population size of MPSO is also variable with the searching state, which mainly helps the algorithm to jump out of local optima. Here, the searching state is estimated as exploration or exploitation simply according to whether the gBest has been updated in $$k$$ consecutive generations or not, where the gBest stands for the position with the best fitness found so far among all the particles in the swarm. The MPSO has been evaluated on 12 unimodal and multimodal Benchmark functions, and the effects of AI and AP strategies are studied. The results show that MPSO improves the performance of the PSO paradigm. The MPSO is also used to find the optimal thresholds by maximizing the Otsu's objective function, and its performance has been validated on 16 standard test images. The experimental results of 30 independent runs illustrate the better solution quality of MPSO when compared with the global particle swarm optimization and standard genetic algorithm. [ABSTRACT FROM AUTHOR]

This paper investigates three-dimensional orbital three-spacecraft-player defense problems. An attacker is about to strike a non-maneuverable asset, while a defender attempts to prevent this attacking in order to protect the asset. It is assumed that both the attacker and the defender have only one chance to maneuver using impulsive thrust. The attacker is not aware of the defender’s participation, while the latter has full information about the former. A hybrid method combined particle swarm optimization with a Newton-Interpolation algorithm is proposed to solve presented orbital defense problems. Numerical results show that the proposed methodology can solve orbital three-player defense problems effectively. Energy consumption of defender is analyzed in detail to tell whether the specified upper bound of defender’s energy is justified. The interesting discovery is the valid departure window of defender in lurk orbit which have important significance for design defender’s strategy in orbital three-player defense problems. [ABSTRACT FROM AUTHOR]

Proper sensor placement is crucial for maximizing the usability of large-scale sensor networks. Specially, the total sensible area covered by a sensor network can be maximized if we optimally arrange all sensors. To address this coverage optimization problem, this paper studies a typical sensor network-camera network. In this network, both locations and orientations of the cameras can be adjusted. An interesting constraint is the moving distance limitation. It transforms the optimization into a constrained problem. To tackle this problem, we investigate as possible solutions three variations of the particle swarm optimization (PSO) algorithm, namely the absorbing PSO, the penalty PSO, and the reflecting PSO. They are tested against several benchmarks. The experiments show that the PSO can be effectively applied on optimizing the coverage of the constrained camera network. And it can be easily adapted for coverage optimization of general sensor networks. The statistical analysis shows that the performances of the above three algorithms are in descending order. The results further prove that the absorbing PSO is an optimal choice for improving the coverage of the aforementioned sensor network. [ABSTRACT FROM AUTHOR]

Particle swarm optimization (PSO) algorithm has shown promising performances on various benchmark functions and engineering optimization problems. However, it is still difficult to achieve a satisfying trade-off between exploration and exploitation for all the optimization problems and different evolving stages. Furthermore, control parameters of some related mechanisms need pre-experience by the requirement of trial-and-error scheme. This paper presents a novel PSO algorithm, which adaptively adopts various search strategies, called Self-adapting Hybrid Strategy PSO (SaHSPS). Unlike some other peer PSO variants, this method dynamically changes the probabilities of different strategies according to their previous successful searching memories, without any additional control parameters. The probabilities of different strategies would be re-initialized according to a proposed dynamic probabilistic model to diverse the population. Besides, particles are updated by probabilistically selected strategies after niching PSO with Ring topology. Moreover, a dynamic updating mechanism by niching PSO is proposed to guarantee the parallel searching capability during the whole evolution process. Thus, this proposed algorithm might be problem-independent and search-stage-independent, yielding more satisfying solutions on various optimization problems. A comprehensive experimental study is conducted on 28 benchmark functions of CEC 2013 special session on real-parameter optimization, including shifted, rotated, multi-modal, high conditioned, expanded and composition problems, compared with several state-of-the-art variants of PSO and differential evolution (DE) algorithms. Comparison results show that SaHSPS obtains outstanding performances on the majority of the test problems. Moreover, a practical engineering problem, real power loss minimization of IEEE 30-bus power system, is used to further evaluate SaHSPS. The numerical results, compared with other stochastic search algorithms, show that SaHSPS could find high-quality solutions with higher probability. [ABSTRACT FROM AUTHOR]

Most image compression algorithms suffer from several drawbacks: high-computational complexity, moderate reconstructed picture qualities, and a variable bit rate. In this paper, an efficient color image quantization technique that depends on an optimized Fuzzy C-means (OFCM) algorithm is proposed. It exploits the optimization capability of the improved artificial fish swarm algorithm to overcome the shortage of Fuzzy C-means algorithm. It uses error diffusion algorithms to obtain perceptually better images after quantization. Experiments are carried out to estimate the performance of the proposed OFCM algorithm in image compression using standard image set. The results indicate that the algorithm can decrease effectively the mean square deviation of color quantization, keep overall arrangement of ideas and part characteristic detail in image reconstruction. The performance efficiency of the proposed technique is compared with those of three other quantization algorithms. The Comparative results confirmed that the OFCM has potential in terms of both accuracy and perceptual quality as compared to recent methods of the literature. [ABSTRACT FROM AUTHOR]

Bare-bones particle swarm optimization (BBPSO) was first proposed in 2003. Compared to the traditional particle swarm optimization, it is simpler and has only a few control parameters to be tuned by users. In this paper, an improved BBPSO algorithm with adaptive disturbance (ABPSO) is studied. By the proposed approaches, each particle has its own disturbance value, which is adaptively decided based on its convergence degree and the diversity of swarm. And an adaptive mutation operator is introduced to improve the global exploration of ABPSO. Moreover, the convergence of ABPSO is analyzed using stochastic process theory by regarding each particle's position as a stochastic vector. A series of experimental trials confirms that the proposed algorithm is highly competitive to other BBPSO-based algorithms, and its performance can be still further improved with the use of mutation. [ABSTRACT FROM AUTHOR]

This paper introduces a new algorithmic nature-inspired approach that uses particle swarm optimization (PSO) with different neighborhood topologies, for successfully solving one of the most computationally complex problems, the permutation flowshop scheduling problem (PFSP). The PFSP belongs to the class of combinatorial optimization problems characterized as NP-hard and, thus, heuristic and metaheuristic techniques have been used in order to find high quality solutions in reasonable computational time. The proposed algorithm for the solution of the PFSP, the PSO with expanding neighborhood topology, combines a PSO algorithm, the variable neighborhood search strategy and a path relinking strategy. As, in general, the structure of the social network affects strongly a PSO algorithm, the proposed method using an expanding neighborhood topology manages to increase the performance of the algorithm. As the algorithm starts from a small size neighborhood and by increasing (expanding) in each iteration the size of the neighborhood, it ends to a neighborhood that includes all the swarm, and it manages to take advantage of the exploration abilities of a global neighborhood structure and of the exploitation abilities of a local neighborhood structure. In order to test the effectiveness and the efficiency of the proposed method, we use a set of benchmark instances of different sizes and compare the proposed method with a number of other PSO algorithms and other algorithms from the literature. [ABSTRACT FROM AUTHOR]

Numerical simulation of proton-exchange membrane fuel cells (PEMFCs) requires an adequate model and precise parameters for reproducing their operational performance quantified by the polarization curve. Bioinspired algorithms are well-suited for optimization. The simulator is stressed by inputting thousands of randomly generated parameters, and hence, a robust numerical model is required. Once the proper model and parameters reproduce the experimental data, they can be used for design improvement. This article proposes a reformulation of a macrohomogeneous mathematical model to provide higher numerical stability to the solutions. We introduce optimization problems for parameter estimation and design optimization by applying three bioinspired algorithms to maximize its performance and minimize the platinum mass loading m Pt . The results are validated by comparing the experimental polarization curves with those simulated from the estimated parameters. We compare a base design's performance with the optimized design for maximum performance. We also compare a base design with the optimized design for minimum m Pt . The results show that the particle swarm optimization requires the lowest computational cost and performs the best in most cases, fitting the experimental data with errors lesser than 10 - 17 . The minimization of m Pt reduces the amount by 42% compared to the base case. [ABSTRACT FROM AUTHOR]

The concept of computational optimization can be used to solve linear and nonlinear Diophantine equations. The goal of this article is to introduce an alternative technique, which is independent of greatest common divisor (g.c.d), to solve linear and nonlinear Diophantine equations by transforming the equations into discrete variable bound constrained optimization problems. To solve these optimization problems, weighted quantum-behaved particle swarm optimization (WQPSO) is developed into discrete variable form. Then, four linear and four nonlinear Diophantine equations are considered from recent literature and solved by using the discrete WQPSO algorithm. Next, the convergence histories of each of the problems are shown. Finally, the results are compared with the same reported by numerous researchers. [ABSTRACT FROM AUTHOR]

Text non-text separation is one of the most essential pre-processing steps for any optical character recognition (OCR) system. As an OCR engine can only process texts, the non-texts present in an input document image are required to be suppressed at the initial level. Therefore, to build a complete OCR system, an efficient text non-text separation module needs to be developed. To this end, we have proposed a texture-based feature descriptor followed by a novel feature selection technique for region-based text non-text classification. First, we have incorporated rotation invariant property with local ternary pattern to form a new texture-based feature descriptor, rotation invariant local ternary pattern (RILTP). Next, a novel feature selection technique is proposed which is a modified version of binary particle swarm optimization (BPSO). For the evaluation of the proposed text non-text classification method, we have initially constructed a database consisting of 690 images of text and non-text regions extracted from 70 pages of RDCL 2015 and 75 pages of RDCL 2017 page segmentation competitions databases. In this database, each class contains 345 data samples. The proposed texture-based feature descriptor has obtained an accuracy of 97.09% on this database. Whereas, after applying BPSO, the feature dimension is reduced by approximately 55% and at the same time, the accuracy reaches 97.5%. Furthermore, in this work, another database is also created from Media team document pages to validate the robustness of this method. The second database comprises 100 text and 100 non-text images. The method has achieved 96.28% accuracy when it is trained with the first database and tested with the second database. The comparative study reveals the robustness and strength of the proposed method as it outnumbers many state-of-the-art texture-based features. Besides, the proposed feature selection method is also compared with various standard feature selection methods, and it has been observed that the proposed one outperforms all those methods considered here for comparison. [ABSTRACT FROM AUTHOR]

Understanding student's thinking ability, strengths, weaknesses, learning behaviour, and their learning capacity are essential considerations in the virtual learning environment (VLE). The major aim of this research study is to design a 'Student Model' on the basis of the individual's 'bio-psychological potential'. In this work, the student's cognitive and personality traits are identified through psychometric inventories such as Benziger thinking style assessment (BTSA) for brain dominance analysis, Kolb's learning style inventory (LSI) for identifying the style of learning, Howard Gardner's MI inventory for multiple intelligence identification and Paul Costa R. Robert McCrae's Big Five personality identification. The proposed model using a fuzzy soft set-based hybrid optimization algorithm for reducing the parameters. The fuzzy softest phase follows fuzzification in which the linguistic variables are formed from the crisp input and generate fuzzy sets further fuzzy soft sets are formed from the formulated fuzzy sets. Finally, the hybrid fuzzy particle swarm-grey wolf optimizer (HFPSGWO) algorithm to accomplish parameter reduction and determine the indispensable parameters and final phase perform student rank analysis. The performance results reveal that the hybrid fuzzy GWO (FGWO) and fuzzy PSO (FPSO)are outperformed when analysing different performances such as fitness function, selection of features, accuracy, and computational time. [ABSTRACT FROM AUTHOR]

Since real-world problems are quite complex, developing more effective methods to optimize them is taken into consideration at the present age. One of the most popular strategies to solve such problems is nature-inspired metaheuristic algorithms which have presented promising solutions in different fields. The JAYA algorithm is an innovative and promising technique for solving a wide range of optimization problems. Hence, this investigation provides a new hybrid algorithm based on JAYA and crow search algorithm (CSA), known as HJCSA, to enhance its performance. In other words, the mechanism of updating position in CSA is integrated with JAYA algorithm to reinforce the convergence and search capabilities of JAYA algorithm. The proposed hybrid algorithm is evaluated through solving 20 well-known benchmark functions, and obtained results are compared with various up-to-date algorithms, including CSA, JAYA, particle swarm optimization (PSO), dragonfly algorithm (DA), grasshopper optimization algorithm (GOA), moth-flame optimization (MFO), and sine–cosine algorithm (SCA). In addition, a real-world engineering problem is optimized by the proposed optimization approach and compared with other published works. The results showed that the suggested optimization algorithm is much more vigorous in escaping from local minima, possessing better convergence, and discovering more accurate solutions. [ABSTRACT FROM AUTHOR]

Grey wolf optimization (GWO) is a meta-heuristic inspired by the social hierarchy and the hunting behaviors observed in wolves. The GWO algorithm has been successfully applied in many fields, such as finance, engineering and industry. However, the GWO has the disadvantages that it is prone to stagnate in local solutions, and the convergence may be slower. In order to overcome these shortcomings, we re-analyzed the hunting behaviors of wolves and observed that there are very frequent communications between leaders and ω-type wolves during the hunting process. This process is called judging prey. Based on this observation, we propose an improved optimization algorithm, termed decision-making grey wolf optimization algorithm (DGWO). At variance with the original GWO, DGWO includes four steps, instead of three: searching for prey, judging prey, encircling prey and attacking prey. The algorithm is tested using 29 well-known benchmark functions and traveling salesman problem, and compared with the GWO algorithm, as well as to the existing improved algorithms random walk grey wolf optimization and modified grey wolf optimization. Results show that including the judging prey stage in DGWO results in a faster convergence and it effectively prevents the algorithm to stop in local optima. [ABSTRACT FROM AUTHOR]

Surgical rescheduling is necessary for adjusting initial schedules on surgery day after emergency demand is realized. While people-oriented medical service has been emphasized these years, the traditional rescheduling scheme which is only in pursuit of a great cost-related performance is no longer desirable since patients and medical staff are also highly involved in rescheduling and their preferences should not be ignored. In order to provide a satisfactory and people-centered rescheduling plan, this study considers the preferences of three involved participants (i.e. the operating room manager, medical staff, and elective patients) while designing a rescheduling plan. Based on prospect theory, we introduce three functions to evaluate three participants' dissatisfaction about rescheduling schemes in terms of their respective preferences. Then a multi-objective rescheduling model is established with multiple resource constraints, emergency lead-time target constraints, and the objective of minimizing the dissatisfaction of three participants caused by rescheduling. A hybrid particle swarm optimization (HPSO) algorithm with two improved strategies—an initial population construction strategy and a local search strategy, is then developed to solve the proposed problem. Several numerical experiments are carried out by leveraging data reported in existing case studies in conjunction with simulated data. The results demonstrate the effectiveness of two improved strategies and show that the proposed HPSO algorithm can provide better Pareto solutions for our problem in comparison with the basic non-dominated sorting genetic algorithm. [ABSTRACT FROM AUTHOR]

Redheaded Agama lizards attack their prey in a well-organized manner. This work models the dynamic foraging behaviour of Agama lizards and their effective way of capturing prey into a mathematical model named as artificial lizard search optimization (ALSO) algorithm. The idea is based on a recent study in which the researchers reported that the lizards control the swing of their tails in a measured manner to redirect angular momentum from their bodies to their tails, stabilizing body attitude in the sagittal plane. A balanced lumping (between body and tail angles) plays a significant role in capturing the prey in a shot. In formulating the optimization problem, a swarm of lizard are considered that are hunting for the prey. To study the performance of the proposed ALSO, it has been simulated. A comparative study is done with some well-known nature-inspired optimization techniques on classical unimodal, multimodal and other benchmark functions. Further, the algorithm is also tested on an object detection application. The result proves the effectiveness of the proposed ALSO algorithm over other nature-inspired state of the art. [ABSTRACT FROM AUTHOR]

Software has made a profound influence in all walks of life. Developing quality software is a major challenge, and the consistency and completeness of the design has a prime role in the development of quality software. Many a times, the process of consistency checking in industries is manual. Artificial intelligence techniques can replace many of these manual efforts to make the development of software easier and cost-effective. Software developers use state diagrams to represent the dynamic behavior in the design stage. We propose a novel application of self regulating particle swarm optimization (SRPSO) algorithm to ensure consistency of state diagrams during the design phase of software development. Inconsistency management is modeled as an optimization problem. In this work, we detect two types of state change inconsistency, incompatible behavior inconsistency and disconnected model inconsistency. A fitness function is defined to detect inconsistency. We make use of the SRPSO algorithm to resolve inconsistency. Detecting inconsistencies in the early stages of software development enables phase containment of errors and prevents errors from being propagated to the code. The proposed approach generates consistent and complete state diagrams leading to accurate code generation, meeting time deadlines, reducing cost of production and easy system maintenance. [ABSTRACT FROM AUTHOR]

In operation of active electric distribution networks, optimal configuration and schedule of distributed generation and reactive power resources are determined. This represents a formidable multi-modal constrained optimisation problem with discrete decision variables. Metaheuristics are the most common approaches for solving this problem. However, due to its multi-modal nature, metaheuristics commonly converge prematurely into local optima and cannot find near-global solutions. In this research, a new particle swarm optimisation (PSO) variant is put forward for finding optimal configuration and schedule of distributed generation and reactive power resources in distribution systems including both dispatchable and renewable distributed energy resources. In the proposed PSO variant, opposition-based learning concept is incorporated into PSO which reduces premature convergence probability through enhancement of swarm leaders. The results of the proposed opposition-based PSO in IEEE 69 bus system indicate its outperformance over conventional PSO, time-varying acceleration coefficient PSO, fractal optimisation algorithm and evolutionary programming. [ABSTRACT FROM AUTHOR]

The swallow swarm optimization (SS) is a challenging method of optimization, which has a quicker convergence speed, not getting caught in the local extreme points. However, the SS suffers from a few shortcomings—(1) the movement speed of particles is not controlled suitably during the search due to the requirement of an inertia weight and (2) the less flexibility of variables does not permit to maintain a balance between the local and the global searches. To solve these problems, a new Levy swallow swarm optimization (SSLY) algorithm with the exploitation capability is proposed. This article also provides an optimal design methodology for the low-pass filter using the suggested SSLY technique. A new objective function is introduced to achieve the maximally flat frequency response, which is another important contribution to the field. The firefly algorithm (FA), the sine cosine algorithm (SCA) and the standard global optimizers—real coded genetic algorithm (GA), conventional particle swarm optimization (PSO), cuckoo search (CS) and SS, are considered for a comparison. The proposed SSLY outperforms the FA, SCA, GA, PSO, CS and SS algorithms. Results authenticate suitability of the proposed algorithm for solving the filter design problems in the FIR domain. [ABSTRACT FROM AUTHOR]

One of the methods for solving optimization problems is applying metaheuristic algorithms that find near to optimal solutions. Dragonfly algorithm is one of the metaheuristic algorithms which search problem space by the inspiration of hunting and emigration behavior of dragonflies in nature. However, it suffers from the premature convergence of the population to an undesirable point in the detection ability (global search). In this research, an improved dragonfly algorithm called BMDA (applying Biogeography-based algorithm, Mexican hat wavelet, and Dragonfly algorithm) is presented to resolve the premature convergence in high workloads by creating a mutation phase based on the combination of the biogeography-based optimization (BBO) migration process and the Mexican hat wavelet transform in dragonfly algorithm (DA). The algorithm was evaluated for the mean error in comparison with standard dragonfly algorithm (DA), Memory-based Hybrid Dragonfly Algorithm (MHDA), chaotic dragonfly algorithm version 9 (CDA9), Adaptive_DA algorithm, bat algorithm (BAT), particle swarm optimization algorithm (PSO), raven roosting optimization (RRO) and whale optimization algorithm (WOA) using the CEC2017 benchmark functions. The implementation results of the proposed BMDA algorithm applying different benchmark functions outweighed the DA-based algorithm, MHDA algorithm, CDA9 algorithm, Adaptive_DA algorithm, BAT algorithm, PSO algorithm, RRO, and WOA algorithms in terms of mean error. [ABSTRACT FROM AUTHOR]

Harris hawks optimization (HHO) is a recently developed meta-heuristic optimization algorithm based on hunting behavior of Harris hawks. Similar to other meta-heuristic algorithms, HHO tends to be trapped in low diversity, local optima and unbalanced exploitation ability. In order to improve the performance of HHO, a novel quasi-reflected Harris hawks algorithm (QRHHO) is proposed, which combines HHO algorithm and quasi-reflection-based learning mechanism (QRBL) together. The improvement includes two parts: the QRBL mechanism is introduced firstly to increase the population diversity in the initial stage, and then, QRBL is added in each population position update to improve the convergence rate. The proposed method will also be helpful to control the balance between exploration and exploitation. The performance of QRHHO has been tested on twenty-three benchmark functions of various types and dimensions. Through comparison with the basic HHO, HHO combined with opposition-based learning mechanism and HHO combined with quasi-opposition-based learning mechanism, the results demonstrate that QRHHO can effectively improve the convergence speed and solution accuracy of the basic HHO and two variants of HHO. At the same time, QRHHO is also better than other swarm-based intelligent algorithms. [ABSTRACT FROM AUTHOR]

There exists a direct need to automate the process of test suite generation to get the most optimal results as testing accounts for more than 40% of total cost. One method to solve this problem is the use of meta-heuristic algorithms which iteratively improve the test data to reach the most optimized test suites. This study focuses on the performance evaluation of six meta-heuristic algorithms namely: hill-climbing algorithm (HCA), particle swarm optimization (PSO), firefly algorithm (FA), cuckoo search algorithm (CS), bat algorithm (BA) and artificial bee colony algorithm (ABC) by using their standard implementation to optimize the path coverage and branch coverage produced by the test data. The goal of the study was to find the best-suited algorithm to narrow down the future research in the field of test automation for path coverage-based optimization approaches. Each algorithm was first implemented to automatically generate test suites based on the program under test. This was followed by the performance evaluation of each algorithm for five programs written in Java. The algorithms were compared using process metrics: average time, best time, worst time and product metrics: path coverage & objective function values of the generated test suites. Results indicated ABC as the best-suited algorithm as it gave the most optimal test suites in reasonable time. BA was found to be the fastest but produced less optimal results. FA was found to be the slowest algorithm, while CS, PSO and HCA performed in between. These results show the relative performance of the six algorithms for this scenario and may be used by the future researchers to narrow down and improve the best performing algorithms for path coverage-based optimization approaches. [ABSTRACT FROM AUTHOR]

In this article, a novel hybrid multi-objective bacterial colony chemotaxis (HMOBCC) algorithm is proposed to solve multi-objective optimization problems. A mechanism of particle swarm optimization is introduced to multi-objective bacterial colony chemotaxis (MOBCC) algorithm to improve the performance of MOBCC algorithm. Also, three other techniques, including dynamic reverse learning operator, external archive multiplying operator and adaptive diversity maintenance operator, are further applied to improve the diversity and convergence of the algorithm. The proposed algorithm is validated using 12 benchmark problems, and three performance measures are implemented for 5 benchmark problems to compare its performance with existing popular algorithms such as MOBCC, multi-objective bacterial colony chemotaxis based on grid algorithm, non-dominated sorting genetic algorithm (NSGA-II) and multi-objective evolutionary algorithm based on decomposition. The results show that the proposed HMOBCC is very effective against existing algorithms. The graphical abstract of this study. [ABSTRACT FROM AUTHOR]

The fruit fly optimization algorithm (FOA) has been developed by inspiring osphresis and vision behaviors of the fruit flies to solve continuous optimization problems. As many researchers know that FOA has some shortcomings, this study presents an improved version of FOA to remove with these shortcomings in order to improve its optimization performance. According to the basic version of FOA, the candidate solutions could not take values those are negative as well as stated in many studies in the literature. In this study, two sign parameters are added into the original FOA to consider not only the positive side of the search space, but also the whole. To experimentally validate the proposed approach, namely signed FOA, SFOA for short, 21 well-known benchmark problems are considered. In order to demonstrate the effectiveness and success of the proposed method, the results of the proposed approach are compared with the results of the original FOA, results of the two different state-of-art versions of particle swarm optimization algorithm, results of the cuckoo search optimization algorithm and results of the firefly optimization algorithm. By analyzing experimental results, it can be said that the proposed approach achieves more successful results on many benchmark problems than the compared methods, and SFOA is presented as more equal and fairer in terms of screening the solution space. [ABSTRACT FROM AUTHOR]

The artificial bee colony (ABC) algorithm is one of the popular swarm intelligence algorithms that is inspired by the forging behavior of honeybee colonies. To improve the convergence precision of the ABC algorithm, accelerate the search speed of finding the best solution and control the balance between exploration and exploitation, we propose an improved double-population ABC algorithm based on heterogeneous comprehensive learning (HCLIABC). In this algorithm, the swarm is divided into exploration-subpopulation named group 1 and exploitation-subpopulation named group 2. Illuminated by particle swarm optimization (PSO), the food source will be updated on all dimensions rather than on a randomly selected dimension. Meanwhile HCL strategy is used to generate the exemplars for two subpopulations. In addition, opposition-based learning is used to improve the quality of initial swarm, and multiplicative weight update method is used to update the selection probability of the double-population in employed bees phase. To evaluate the remarkable performance of the improved algorithm, we conduct comparative experiments of 18 unimodal, multimodal, and rotated benchmark functions on dimensions 30 and 100. Computational results demonstrate that HCLIABC can effectively prevent premature convergence and produce competitive optimization precision and convergence speed compared with several popular and classic DE, PSO and ABC variants. [ABSTRACT FROM AUTHOR]

An intelligent integrated method is proposed for optimizing the head grade and dressing grade in the mining and ore-dressing management of metal mines, beginning with the establishment of a nonlinear constrained optimization model with the objective function of economic benefit, two constraints comprising of the resource utilization rate and the output of concentrate, along with head grade and dressing grade as the decision variables. Particle swarm optimization (PSO) algorithm is then integrated with artificial neural networks to create a PSO-ANN algorithm capable of identifying the optimal grade combination. The outer layer of PSO-ANN uses the PSO algorithm to carry out a global search, with the head grade and dressing grade being combined as swarm particles for evolutionary computation. The constraint handling techniques of feasibility-based rules are used to update the historical best location of each particle ( pbest) and the global best location of the swarm ( gbest) to guide the particles toward the optimum. The inner layer uses regression model, BPNN and RBFNN to calculate the loss rate, ore-dressing metal recovery rate and costs, respectively, to facilitate the further calculation of the resource utilization rate, the concentrate output and the economic benefit of each particle. Finally, the proposed method is tested by carrying out a case study based upon Daye Iron Mine to indicate its effectiveness and reliability. [ABSTRACT FROM AUTHOR]

Rational path optimization of weld joints sequence can reduce welding time, improve the welding quality and productivity in robot welding process, especially when the number of weld joints is large. In this article, definition of welding robot path optimization is studied first. Then, PSO is used to solve welding robot path optimization after algorithm discretization and global optimization capability improvement based on elite strategy. The shortest path length and welding deformation were considered as the optimization criteria. For double welding robot welding path optimization, the influence with each other should be avoided for two welding robots. Hence, an optimization strategy was proposed to solve double welding robots path optimization problem with constrains of avoiding mutual influence. Simulation results show that the proposed optimization algorithm and strategy can promise desired optimization effect. [ABSTRACT FROM AUTHOR]

This research proposes a revised discrete particle swarm optimization (RDPSO) to solve the permutation flow-shop scheduling problem with the objective of minimizing makespan (PFSP-makespan). The candidate problem is one of the most studied NP-complete scheduling problems. RDPSO proposes new particle swarm learning strategies to thoroughly study how to properly apply the global best solution and the personal best solution to guide the search of RDPSO. A new filtered local search is developed to filter the solution regions that have been reviewed and guide the search to new solution regions in order to keep the search from premature convergence. Computational experiments on Taillard's benchmark problem sets demonstrate that RDPSO significantly outperforms all the existing PSO algorithms. [ABSTRACT FROM AUTHOR]

In order to overcome the premature convergence in particle swarm optimization (PSO), we introduce dynamical crossover, a crossover operator with variable lengths and positions, to PSO, which is briefly denoted as CPSO. To get rid of the drawbacks of only finding the convex clusters and being sensitive to the initial points in $$k$$ -means algorithm, a hybrid clustering algorithm based on CPSO is proposed. The difference between the work and the existing ones lies in that CPSO is firstly introduced into $$k$$ -means. Experimental results performing on several data sets illustrate that the proposed clustering algorithm can get completely rid of the shortcomings of $$k$$ -means algorithms, and acquire correct clustering results. The application in image segmentation illustrates that the proposed algorithm gains good performance. [ABSTRACT FROM AUTHOR]

A new hybrid adaptive algorithm based on particle swarm optimization (PSO) is designed, developed and applied to the high school timetabling problem. The proposed PSO algorithm is used to create feasible and efficient timetables for high schools in Greece. Experiments with real-world data coming from different high schools have been conducted to show the efficiency of the proposed PSO algorithm. As well as that, the algorithm has been compared with four other effective techniques found in the literature to demonstrate its efficiency and superior performance. In order to have a fair comparison with these algorithms, we decided to use the exact same input instances used by these algorithms. The proposed PSO algorithm outperforms, in most cases, other existing attempts to solve the same problem as shown by experimental results. [ABSTRACT FROM AUTHOR]

Large scale continuous optimization problems are more relevant in current benchmarks since they are more representative of real-world problems (bioinformatics, data mining, etc.). Unfortunately, the performance of most of the available optimization algorithms deteriorates rapidly as the dimensionality of the search space increases. In particular, particle swarm optimization is a very simple and effective method for continuous optimization. Nevertheless, this algorithm usually suffers from unsuccessful performance on large dimension problems. In this work, we incorporate two new mechanisms into the particle swarm optimization with the aim of enhancing its scalability. First, a velocity modulation method is applied in the movement of particles in order to guide them within the region of interest. Second, a restarting mechanism avoids the early convergence and redirects the particles to promising areas in the search space. Experiments are carried out within the scope of this Special Issue to test scalability. The results obtained show that our proposal is scalable in all functions of the benchmark used, as well as numerically very competitive with regards to other compared optimizers. [ABSTRACT FROM AUTHOR]