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The particle swarm optimization (PSO) algorithm has been the object of many studies and modifications for more than 25 years. Ranging from small refinements to the incorporation of sophisticated novel ideas, the majority of modifications proposed to this algorithm have been the result of a manual process in which developers try new designs based on their own knowledge and expertise. However, manually introducing changes is very time consuming and makes the systematic exploration of all the possible algorithm configurations a difficult process. In this article, we propose to use automatic design to overcome the limitations of having to manually find performing PSO algorithms. We develop a flexible software framework for PSO, called PSO-X, which is specifically designed to integrate the use of automatic configuration tools into the process of generating PSO algorithms. Our framework embodies a large number of algorithm components developed over more than 25 years of research that have allowed PSO to deal with a large variety of problems, and uses irace, a state-of-the-art configuration tool, to automatize the task of selecting and configuring PSO algorithms starting from these components. We show that irace is capable of finding high-performing instances of PSO algorithms never proposed before. [ABSTRACT FROM AUTHOR]

Particle Swarm Optimization (PSO) is a meta-heuristic algorithm to determine the social behavior of different insects and to implement their optimization techniques in various real-life optimization problems. Swarms use these optimization techniques to lead their daily life for communicating with other swarms, foraging and mating activities and travelling to their destination. PSO algorithms used these methods to solve optimization problems in several fields of computer science. However, real-life problems have different parameters and problem space is not the same for all. That's why the original version of PSO cannot be applied in every problem. PSO algorithm changed its needful features to solve new problems based on problem specification. Different versions of PSO come up with different optimization methods focusing on specific problem space. In each version, many methods or approaches have been proposed by many researchers and their optimization strategy and performance of finding an optimal solution can be different. A study on extensive analysis of different variants of PSO algorithms should have a great value for prospective researchers focusing in this field. In this research, we will explain optimization techniques of assorted versions of PSO algorithm (BPSO, DPSO, PSO-DE, PSO-NE and HPSO) and their variants. Then analyze their performance and present comparative results. We have compared the performance of each variant in terms of benchmark functions and other related algorithms. From this research, we will have acquainted which version is better for which type of optimization problems. [ABSTRACT FROM AUTHOR]

The accuracy and stability of relative pose estimation of an autonomous underwater vehicle (AUV) and a target depend on whether the characteristics of the underwater image can be accurately and quickly extracted. In this paper, a whale optimization algorithm (WOA) based on lateral inhibition (LI) is proposed to solve the image matching and vision-guided AUV docking problem. The proposed method is named the LI-WOA. The WOA is motivated by the behavior of humpback whales, and it mainly imitates encircling prey, bubble-net attacking and searching for prey to obtain the globally optimal solution in the search space. The WOA not only balances exploration and exploitation but also has a faster convergence speed, higher calculation accuracy and stronger robustness than other approaches. The lateral inhibition mechanism can effectively perform image enhancement and image edge extraction to improve the accuracy and stability of image matching. The LI-WOA combines the optimization efficiency of the WOA and the matching accuracy of the LI mechanism to improve convergence accuracy and the correct matching rate. To verify its effectiveness and feasibility, the WOA is compared with other algorithms by maximizing the similarity between the original image and the template image. The experimental results show that the LI-WOA has a better average value, a higher correct rate, less execution time and stronger robustness than other algorithms. The LI-WOA is an effective and stable method for solving the image matching and vision-guided AUV docking problem. [ABSTRACT FROM AUTHOR]

In a dynamic environment with wind forces and tornadoes, eliminating fluctuations and noise is critical to get the optimal results. Avoiding collision and simultaneous arrival of multiple unmanned aerial vehicles (multi-UAVs) is also a great problem. This paper addresses the cooperative path planning of multi-UAVs with in a dynamic environment. To deal with the aforementioned issues, we combine the maximum–minimum ant colony optimization (MMACO) and Cauchy Mutant (CM) operators to make a bio-inspired optimization algorithm. Our proposed algorithm eliminates the limitations of classical ant colony optimization (ACO) and MMACO, which has the issues of the slow convergence speed and a chance of falling into local optimum. This paper chooses the CM operator to enhance the MMACO algorithm by comparing and examining the varying tendency of fitness function of the local optimum position and the global optimum position when taking care of multi-UAVs path planning problems. It also makes sure that the algorithm picks the shortest route possible while avoiding collision. Additionally, the proposed method is more effective and efficient when compared to the classic MMACO. Finally, the simulation experiment results are performed under the dynamic environment containing wind forces and tornadoes. [ABSTRACT FROM AUTHOR]

Searching saddle points on the potential energy surface is a challenging problem in the rare event. When there exist multiple saddle points, sampling different initial guesses are needed in local search methods in order to find distinct saddle points. In this paper, we present a novel global optimization-based dimer method (GOD) to efficiently search saddle points by coupling ant colony optimization (ACO) algorithm with optimization-based shrinking dimer (OSD) method. In particular, we apply OSD method as a local search algorithm for saddle points and construct a pheromone function in ACO to update the global population. By applying a two-dimensional example and a benchmark problem of seven-atom island on the (111) surface of an FCC crystal, we demonstrate that GOD shows a significant improvement in computational efficiency compared with OSD method. Our algorithm is the first try to apply the global optimization technique in searching saddle points and it offers a new framework to open up possibilities of adopting other global optimization methods. [ABSTRACT FROM AUTHOR]

A constrained particle swarm optimization algorithm (C-PSO) is introduced and modified for hyperelastic and visco-hyperelastic characterization of bovine brain tissue at three different strain rates. Using the elasticity compatibility and Drucker's stability criterion, the constraints of the hyperelastic and visco-hyperelastic models are identified and implemented in the C-PSO algorithm and its performance is compared with the classic curve fitting algorithms including Levenberg-Marquardt and trust region reflective. The accuracy of the C-PSO was found to be superior for visco-hyperelastic characterization, as for some strain rates, the trust region reflective algorithm failed to provide a reasonable approximation. [ABSTRACT FROM AUTHOR]

Estimation of software cost and effort is of prime importance in software development process. Accurate and reliable estimation plays a vital role in successful completion of the project. To estimate software cost, various techniques have been used. Constructive Cost Model (COCOMO) is amongst most prominent algorithmic model used for cost estimation. Different versions of COCOMO consider different types of parameters affecting overall cost. Parameters involved in estimation using COCOMO possess vagueness which introduces some degree of uncertainty in algorithmic modelling. The concept of fuzzy logic can deal with uncertainty involved in Intermediate COCOMO cost driver measurements via Fuzzy Inference System (FIS). In the proposed research, an effort has been made wherein, for each cost driver, an FIS is designed to calculate the corresponding effort multiplier. Proposed research provides an insight through evolutionary-based optimization techniques to optimize fuzzy logic-based COCOMO using Particle Swarm Optimization Algorithm. The magnitude of relative error and its mean, calculated using COCOMO NASA2 and COCOMONASA datasets are used as evaluation metrics to validate the proposed model. The model outperforms when compared to other optimization techniques like Genetic Algorithm. [ABSTRACT FROM AUTHOR]

This paper presents an energy-efficient Modified Distance Vector Hop algorithm using Teaching-Learning-Based Optimization, viz. MDV-TLBO, which is range-free, distributed localization algorithm for wireless sensor network (WSN). In the proposed algorithm, the hop size of anchor node is modified by adding correction factor. The concept of collinearity is introduced in this paper to reduce location errors caused by anchor nodes which are collinear. TLBO is used to enhance the localization accuracy, which is parameter-free, efficient optimization technique. Target nodes estimate their final coordinates after location upgradation procedure. In MDV-TLBO, anchor nodes communicate only one time with target nodes to broadcast their location. Hop size modification, optimal selection of anchor nodes, location optimization, and location upgradation are done at target node level, resulting considerable reduction in communication between nodes, due to which energy consumption of the nodes has been significantly reduced. To show the applicability of proposed algorithm in real scenarios, radio irregularity model is considered. Simulation results show that our proposed algorithm achieves better localization accuracy, high positioning coverage with less energy consumption in comparison of the existing improved DV-Hop algorithms. [ABSTRACT FROM AUTHOR]

The inverse heat transfer problem associated with constructing multilayer material shells cloaking the presence of a cylindrical body in an externally applied temperature field is studied. As the original mathematical model, the steady-state heat equation for an anisotropic shell is used. With the help of the optimization method, this inverse problem is reduced to the corresponding control problem. A numerical algorithm of its solution based on the particle swarm optimization is proposed, and the results of numerical experiments are discussed. [ABSTRACT FROM AUTHOR]

A new hyperspectral unmixing algorithm is proposed based on the normal compositional model (NCM) to estimate the endmembers and abundance parameters jointly in this paper. The NCM considers the hyperspectral imaging as a stochastic process and interprets each pixel value as a random vector, which is linearly mixed by the endmembers. More precisely, these endmembers are also treated as random variables as opposed to deterministic values in order to capture spectral variability that is not well described by the linear mixing model (LMM). However, the higher complexity of such an unmixing model leads to more difficulty in parameter estimation. A particle swarm optimization-expectation maximization (PSO-EM) algorithm, a “winner-take-all” version of the EM, is proposed to solve the parameter estimation problem, which employs a partial E step. The main contribution of the proposed PSO-EM is making optimum use of particle swarm optimization method (PSO) in the partial E step, which solves the difficulty of the integrals in the NCM model. The performance of the proposed methodology is evaluated through synthetic and real data experiments. Our obtained results demonstrate the superior performance of PSO-EM compared to other NCM-based as well as LMM-based methods. [ABSTRACT FROM PUBLISHER]

An Environmental Adaption Method (EAM) has been recently proposed in which, each solution updates its structure on the basis of current environmental fitness and its own fitness. EAM uses a new operator known as adaption operator. In this paper, an improved EAM, henceforth called as IEAM has been proposed and proper tuning of parameters of algorithm has been done to find the optimal solution in minimum time. IEAM has been tested on a set of benchmark functions. Results show the superiority of IEAM over PSO-TVAC, SADE and other optimization algorithms. Even for complex functions, the performance of IEAM is very good. In addition to this, IEAM has also been applied to software testing to generate test cases for white box testing. [ABSTRACT FROM AUTHOR]

Hyperspectral image classification is one of the most important complicated tasks in recent years mainly due to the diversified pixels of present data in which only limited information is known prior. To solve this problem, in this work a combined pixel-wise characterization framework is applied to hyperspectral image samples. In this work, a mixed pixel-wise characterization is presented for utilization of mixing both the spectral and the spatial information. The proposed work comprises of three major steps: Spectral gradient enhancement, spectral-spatial classification obtained from the Support vector machine-Fuzzy Sigmoid Kernel (SVM-FSK) and probability estimation based on Hidden Markov Model (HMM). To improve the gradient level of spatial information, this work uses Improved Empirical Mode Decomposition (IEMD) with PSO (IEMD-PSO) to increase the mixed pixel wise SVM -FSK classification accuracy. Apply EMD method to enhance the gradient level of spatial data to separately identifiable of intrinsic mode functions (IMFs) of every one of spectral band, weight values of IMFs are calculated by using Particle swarm optimization (PSO).The obtained spectral and spatial information learns the probability value from HMM and mixed pixel-wise SVM-FSK characterization methods to find number of mixed components in each pixel. The proposed mixed pixel-wise probabilistic characterization for SVM-FSK and spectral gradient enhancement method IEMD-PSO shows improved classification accuracy in terms of parameters like overall accuracy, standard deviation and mean. The proposed methods can be compared with existing methods such as an Empirical Mode Decomposition (EMD), Empirical Mode Decomposition -Genetic Algorithm (EMD-GA) methods and Support Vector Machine -Radial Basis function (SVM-RBF). [ABSTRACT FROM AUTHOR]

Designing generic problem solvers that perform well across a diverse set of problems is a challenging task. In this work, we propose a hyper-heuristic framework to automatically generate an effective and generic solution method by utilizing grammatical evolution. In the proposed framework, grammatical evolution is used as an online solver builder, which takes several heuristic components (e.g., different acceptance criteria and different neighborhood structures) as inputs and evolves templates of perturbation heuristics. The evolved templates are improvement heuristics, which represent a complete search method to solve the problem at hand. To test the generality and the performance of the proposed method, we consider two well-known combinatorial optimization problems: exam timetabling (Carter and ITC 2007 instances) and the capacitated vehicle routing problem (Christofides and Golden instances). We demonstrate that the proposed method is competitive, if not superior, when compared to state-of-the-art hyper-heuristics, as well as bespoke methods for these different problem domains. In order to further improve the performance of the proposed framework we utilize an adaptive memory mechanism, which contains a collection of both high quality and diverse solutions and is updated during the problem solving process. Experimental results show that the grammatical evolution hyper-heuristic, with an adaptive memory, performs better than the grammatical evolution hyper-heuristic without a memory. The improved framework also outperforms some bespoke methodologies, which have reported best known results for some instances in both problem domains. [ABSTRACT FROM AUTHOR]

We consider the problem of representing a generally anisotropic elasticity tensor, which might be obtained from physical measurements, by a tensor belonging to a chosen material symmetry class, so-called ‘effective tensor'. Following previous works on the subject, we define this effective tensor as the solution of a global optimization problem for the Frobenius distance function. For all nontrivial symmetry classes, except isotropy, this problem is nonlinear, since it involves all orientations of the symmetry groups. We solve the problem using a metaheuristic method called particle-swarm optimization and employ quaternions to parametrize rotations in 3-space to improve computational efficiency. One advantage of this approach over previously used plot-guided local methods and exhaustive grid searches is that it allows us to solve a large number of instances of the problem in a reasonable time. As an application, we can use Monte-Carlo method to analyze the uncertainty of the orientation and elasticity parameters of the effective tensor resulting from the uncertainty of the given tensor, which may be caused, for example, by measurement errors. [ABSTRACT FROM PUBLISHER]

Retina-like sensors maximize both field of view and resolution in addition to economizing on pixel count, so they play an important role in both biological and machine vision. A new retina-like sensor model for compensating motion blur introduced by forward motion imaging is proposed. Next, the determination of pixel arrangement of a retina-like sensor according to visual task requirements is formulated into a multiobjective optimization problem. Then, three retina-like sensors are designed to meet different visual task requirements using the particle swarm optimization algorithm. The results are robust and approximate to design criteria. [ABSTRACT FROM AUTHOR]

Developing techniques for optimal allocation of limited resources to a set of activities has received increasing attention in recent years. In this paper, an efficient resource allocation scheme based on particle swarm optimization (PSO) is developed. Different from many existing evolutionary algorithms for solving resource allocation problems (RAPs), this PSO algorithm incorporates a novel representation of each particle in the population and a comprehensive learning strategy for the PSO search process. The novelty of this representation lies in that the position of each particle is represented by a pair of points, one on each side of the constraint hyper-plane in the problem space. The line joining these two points intersects the constraint hyper-plane and their intersection point indicates a feasible solution. With the evaluation value of the feasible solution used as the fitness value of the particle, such a representation provides an effective way to ensure the equality resource constraints in RAPs are met. Without the distraction of infeasible solutions, the particle thus searches the space smoothly. In addition, particles search for optimal solutions by learning from themselves and their neighborhood using the comprehensive learning strategy, helping prevent premature convergence and improve the solution quality for multimodal problems. This new algorithm is shown to be applicable to both single-objective and multiobjective RAPs, with performance validated by a number of benchmarks and by a real-world bed capacity planning problem. Experimental results verify the effectiveness and efficiency of the proposed algorithm. [ABSTRACT FROM PUBLISHER]

A novel optimization paradigm, called Community of Scientists Optimization (CoSO), is presented in this paper. The approach is inspired to the behaviour of a community of scientists interacting, pursuing for research results and foraging the funds needed to held their research activities. The CoSO metaphor can be applied to general optimization domains, where optimal solutions emerge from the collective behaviour of a distributed community of interacting autonomous entities. The CoSO framework presents analogies and remarkable differences with other evolutionary optimization approaches: swarm behaviour, foraging and selection mechanism based on research funds competition, dynamically evolving multicapacity communication channels realized by journals and evolving population size regulated by research management strategies. Experiments and comparisons on benchmark problems show the effectiveness of the approach for numerical optimization. CoSO, with the design of appropriate foraging and competition strategies, also represents a great potential as a general meta-heuristic for applications in non-numerical and agent-based domains. [ABSTRACT FROM AUTHOR]

The effectiveness of sliding mode control on the seismic response of an isolated bridge with columns of irregular heights, which exhibit hysteretic behaviors at both the columns and isolators, is studied. The bridge of concern consists of a two-span continuous deck and three columns of irregular heights, adjoining two single-span approaches each at the two ends. The irregular isolated bridge is idealized by an equivalent model to reduce the number of degrees of freedom involved. Compared with typical isolated bridges, the irregular isolated bridge has more poles of sliding surface, which dominates the dynamic characteristics of the controlled system and should be determined for the sliding mode control. The particle swarm optimization-simulated annealing (PSO-SA) hybrid searching algorithm is thus employed and shown to outperform the PSO algorithm and a parametric approach in finding the best sliding surface. Numerical simulations reveal that the sliding mode control together with the PSO-SA hybrid searching algorithm provides a simple and powerful technique for controlling the nonlinear seismic responses of irregular isolated bridges. Such a technique combining the control and optimization technology can be applied to practical bridges or structures, which are generally complicated and should be idealized by sophisticated numerical models. [ABSTRACT FROM AUTHOR]

This paper applies particle swarm optimization (PSO) to find the most possible failure surface in stability analysis of homogeneous soil slopes. The stability of slopes is an important issue on geotechnical engineering. This problem includes two general concepts, factor of safety (FOS) and the critical slip surface (CSS). The resultant ratio of dividing strengthens forces by driving forces is called FOS. The critical slip surface is defined as a failure surface with the minimum value of FOS among all candidates. Regarding to the vast number of trial slip surfaces and the non-linear nature of equation of FOS, a global optimization algorithm is needed to locate CSS. As an optimization technique, the original version of PSO with little improvements in initial parameters is used. With this aim in view, we developed a computer code to find CSS by particle swarm optimization. Moreover, a sensitivity analysis is conducted to find the optimum values of initial parameters of PSO. Finally the effectiveness and efficiency of PSO code is verified and compared with the benchmark examples from the literature. The results demonstrated the ability of PSO to find CSS with better outcomes than former methods. [ABSTRACT FROM AUTHOR]

A novel dynamic multistage hybrid swarm intelligence optimization algorithm is introduced, which is abbreviated as DM-PSO-ABC. The DM-PSO-ABC combined the exploration capabilities of the dynamic multiswarm particle swarm optimizer (PSO) and the stochastic exploitation of the cooperative artificial bee colony algorithm (CABC) for solving the function optimization. In the proposed hybrid algorithm, the whole process is divided into three stages. In the first stage, a dynamic multiswarm PSO is constructed to maintain the population diversity. In the second stage, the parallel, positive feedback of CABC was implemented in each small swarm. In the third stage, we make use of the particle swarm optimization global model, which has a faster convergence speed to enhance the global convergence in solving the whole problem. To verify the effectiveness and efficiency of the proposed hybrid algorithm, various scale benchmark problems are tested to demonstrate the potential of the proposed multistage hybrid swarm intelligence optimization algorithm. The results show that DM-PSO-ABC is better in the search precision, and convergence property and has strong ability to escape from the local suboptima when compared with several other peer algorithms. [ABSTRACT FROM AUTHOR]

Incremental social learning (ISL) was proposed as a way to improve the scalability of systems composed of multiple learning agents. In this paper, we show that ISL can be very useful to improve the performance of population-based optimization algorithms. Our study focuses on two particle swarm optimization (PSO) algorithms: a) the incremental particle swarm optimizer (IPSO), which is a PSO algorithm with a growing population size in which the initial position of new particles is biased toward the best-so-far solution, and b) the incremental particle swarm optimizer with local search (IPSOLS), in which solutions are further improved through a local search procedure. We first derive analytically the probability density function induced by the proposed initialization rule applied to new particles. Then, we compare the performance of IPSO and IPSOLS on a set of benchmark functions with that of other PSO algorithms (with and without local search) and a random restart local search algorithm. Finally, we measure the benefits of using incremental social learning on PSO algorithms by running IPSO and IPSOLS on problems with different fitness distance correlations. [ABSTRACT FROM AUTHOR]

In recent years, there has been a growing interest in the study of particle swarm optimization (PSO) in dynamic environments. This paper presents a new PSO model, called PSO with composite particles (PSO-CP), to address dynamic optimization problems. PSO-CP partitions the swarm into a set of composite particles based on their similarity using a “worst first” principle. Inspired by the composite particle phenomenon in physics, the elementary members in each composite particle interact via a velocity-anisotropic reflection scheme to integrate valuable information for effectively and rapidly finding the promising optima in the search space. Each composite particle maintains the diversity by a scattering operator. In addition, an integral movement strategy is introduced to promote the swarm diversity. Experiments on a typical dynamic test benchmark problem provide a guideline for setting the involved parameters and show that PSO-CP is efficient in comparison with several state-of-the-art PSO algorithms for dynamic optimization problems. [ABSTRACT FROM AUTHOR]

Particle swarm optimization (PSO) is a population-based swarm intelligence algorithm and it has attracted a large number of widespread researchers. As a branch of PSO, a probabilistic PSO algorithm, which is quantum-behaved PSO (QPSO), was proposed on the quantum mechanics and trajectory analysis of PSO. QPSO shines for its simplicity, easy implementation, and fine search ability. QPSO has also gained many researchers on its improvements and has been shown to offer good performance in a variety of applications. This paper attempts to give a compendious and timely review on QPSO by categorizing the publications on the improvements and applications. [ABSTRACT FROM AUTHOR]

During the last decade, many variants of the original particle swarm optimization (PSO) algorithm have been proposed. In many cases, the difference between two variants can be seen as an algorithmic component being present in one variant but not in the other. In the first part of the paper, we present the results and insights obtained from a detailed empirical study of several PSO variants from a component difference point of view. In the second part of the paper, we propose a new PSO algorithm that combines a number of algorithmic components that showed distinct advantages in the experimental study concerning optimization speed and reliability. We call this composite algorithm Frankenstein's PSO in an analogy to the popular character of Mary Shelley's novel. Frankenstein's PSO performance evaluation shows that by integrating components novel ways effective optimizers can be designed. [ABSTRACT FROM AUTHOR]

Evolutionary algorithms are often successfully applied to hard optimization problems. However, besides rules of thumb and experience, trial and error is still the leading design technique for evolutionary algorithms. A profound theoretical foundation guiding those applications is still missing. This article outlines a networked understanding of evolutionary algorithms. As a first step towards that goal, it reviews and interrelates major theoretical results and working principles in order to give an extensive insight into the internal processing of evolutionary algorithms. This not only helps to understand success and failure of evolutionary algorithms in applications but, in addition, could lead to a theory-guided design process enrichening and relieving today's heuristic techniques. [ABSTRACT FROM AUTHOR]

We compare a first‐order stochastic swarm intelligence model called consensus‐based optimization (CBO), which may be used for the global optimization of a function in multiple dimensions, to other particle swarm algorithms for global optimization. CBO allows for passage to the mean‐field limit resulting in a nonlocal, degenerate, parabolic PDE. Exploiting tools from PDE analysis, it is possible to rigorously prove convergence results for the algorithm (see [3]). In the present article we discuss numerical results obtained with the Particle Swarm Optimization (PSO) [4], Wind‐Driven Optimization (WDO) [6] and CBO and show that CBO leads to very competitive results. [ABSTRACT FROM AUTHOR]

In the modeling of spin‐crossing reactions, it has become popular to directly explore the spin‐adiabatic surfaces. Specifically, through constructing spin‐adiabatic states from a two‐state Hamiltonian (with spin‐orbit coupling matrix elements) at each geometry, one can readily employ advanced geometry optimization algorithms to acquire a "transition state" structure, where the spin crossing occurs. In this work, we report the implementation of a fully‐variational spin‐adiabatic approach based on Kohn‐Sham density functional theory spin states (sharing the same set of molecular orbitals) and the Breit‐Pauli one‐electron spin‐orbit operator. For three model spin‐crossing reactions (predissociation of N2O, singlet‐triplet conversion in CH2, and CO addition to Fe(CO)4), the spin‐crossing points were obtained. Our results also indicated the Breit‐Pauli one‐electron spin‐orbit coupling can vary significantly along the reaction pathway on the spin‐adiabatic energy surface. On the other hand, due to the restriction that low‐spin and high‐spin states share the same set of molecular orbitals, the acquired spin‐adiabatic energy surface shows a cusp (ie, a first‐order discontinuity) at the crossing point, which prevents the use of standard geometry optimization algorithms to pinpoint the crossing point. An extension with this restriction removed is being developed to achieve the smoothness of spin‐adiabatic surfaces. [ABSTRACT FROM AUTHOR]