Species-based Particle Swarm Optimization Research Articles

A Species-based Particle Swarm Optimization with Adaptive Population Size and Deactivation of Species for Dynamic Optimization Problems

Population clustering methods, which consider the position and fitness of individuals to form sub-populations in multi-population algorithms, have shown high efficiency in tracking the moving global optimum in dynamic optimization problems. However, most of these methods use a fixed population size, making them inflexible and inefficient when the number of promising regions is unknown. The lack of a functional relationship between the population size and the number of promising regions significantly degrades performance and limits an algorithm’s agility to respond to dynamic changes. To address this issue, we propose a new species-based particle swarm optimization with adaptive population size and number of sub-populations for solving dynamic optimization problems. The proposed algorithm also benefits from a novel systematic adaptive deactivation component that, unlike the previous deactivation components, adapts the computational resource allocation to the sub-populations by considering various characteristics of both the problem and the sub-populations. We evaluate the performance of our proposed algorithm for the Generalized Moving Peaks Benchmark and compare the results with several peer approaches. The results indicate the superiority of the proposed method.

Tracking of Moving Sources in a Reverberant Environment Using Evolutionary Algorithms

This paper describes a source tracking technique in a reverberant environment using a new combination of an adaptive species-based particle swarm optimization (ASPSO) algorithm and a multisignal classification (MUSIC) algorithm. To mitigate the effects of reverberation, an insightful dereverberation method based on an online autoregressive (AR) array and a minimum variance distortionless response (MVDR) beamformer is developed to dereverberate the microphone signal prior to direction of arrival (DOA) estimation using MUSIC. On the basis of several evolutionary schemes, ASPSO enables rapid tracking by finding local maxima in the MUSIC pseudospectrum. In the ASPSO algorithm, particles are divided into different species, where each species is associated with a sound source. As the sound source moves, the DOA information is dynamically updated using ASPSO, in which the inertia weight decreases progressively to prevent premature convergence. Two update rules for adapting the filter coefficients are employed for drastically moving sources. Simulations and experiments are conducted using a circular microphone array to validate the proposed ASPSO with AR (ASPSO-AR) algorithm. The results demonstrate that ASPSO-AR requires one-third of the processing time of the grid search (GS) method. In addition, the root-mean-square error (RMSE) of the ASPSO-AR algorithm is 10° less than that of the GS method.

Niching particle swarm optimization techniques for multimodal buckling maximization of composite laminates

It is natural to extend conventional unimodal optimization to challenging multimodal optimization design of composite structures by means of emergent niching particle swarm optimization (PSO), due to multimodal characteristics of composite structures by nature. The advanced multimodal PSO algorithms adopted in the present study include the species-based PSO (SPSO), the fitness Euclidean-distance ratio based PSO (FER-PSO), the ring topology based PSO and the Euclidean distance-based locally informed particle swarm (LIPS) optimizer, which are applied to a multimodal buckling maximization problem of composite panels. SPSO, FER-PSO, the ring topology based PSO and the variant of LIPS succeed to simultaneously indentify not only the first-best-fitness solutions (the global optima) but also the second-best-fitness solutions (the global sub-optima) to this buckling optimization design in a single optimization process. The fifteen second-best-fitness solutions are discovered for the first time and the buckling-resistance difference between the seven first-best-fitness solutions and the fifteen second-best-fitness solutions is definitely negligible from the viewpoint of practical applications. Based on the new findings of twenty-two acceptable solutions, this specific buckling maximization design of composite panels can be served as a benchmark problem of real-world multimodal optimization. In terms of the statistical outcomes on algorithmic performance assessments, the ring topology based PSO without any niching parameter is suitable and reliable for multimodal optimization of composite structures in comparison with SPSO, FER-PSO and the variant of LIPS.

Species-based Particle Swarm Optimizer enhanced by memory for dynamic optimization

Both the species strategy and the memory scheme are efficient methods for addressing dynamic optimization problems. However, the combination of these two efficient techniques has scarcely been studied. Thus, this paper focuses on how to hybridize these two methods. In this paper, a new swarm updating method is proposed to enhance a representative species-based algorithm, i.e., SPSO (Species-based Particle Swarm Optimization), and the new algorithm is named MSPSO. MSPSO has two characteristics. First, the number of replaced particles in the current swarm is set adaptively according to the number of species. To not substantially destroy the exploitation capability of each species, no more than one particle in each species is replaced by the memory. Second, the retrieved memory particles are categorized according to their fitness values and their distances to the seed of the closest species. Aimed at enhancing the search in both promising areas and existing species, each category is processed by different operations. The MPB, Cyclic MPB and DRPBG are used to test the performance of MSPSO. Experimental results demonstrate that MSPSO is competitive for dynamic optimization problems.

Investigation of the equality constraint effect on the reduction of the rotational ambiguity in three-component system using a novel grid search method

The obtained results by soft modeling multivariate curve resolution methods often are not unique and are questionable because of rotational ambiguity. It means a range of feasible solutions equally fit experimental data and fulfill the constraints. Regarding to chemometric literature, a survey of useful constraints for the reduction of the rotational ambiguity is a big challenge for chemometrician. It is worth to study the effects of applying constraints on the reduction of rotational ambiguity, since it can help us to choose the useful constraints in order to impose in multivariate curve resolution methods for analyzing data sets. In this work, we have investigated the effect of equality constraint on decreasing of the rotational ambiguity. For calculation of all feasible solutions corresponding with known spectrum, a novel systematic grid search method based on Species-based Particle Swarm Optimization is proposed in a three-component system.

Chaotic species based particle swarm optimization algorithms and its application in PCB components detection

An improved particle swarm optimizer using the notion of chaos and species is proposed for solving a template matching problem which is formulated as a multimodal optimization problem. Template matching is one of the image comparison techniques. This technique is widely applied to determine the existence, location and alignment of a component within a captured image in the printed circuit board (PCB) industry where 100% quality assurance is always required. In this research, an efficient auto detection method using a multiple templates matching technique for PCB components detection is described. The new approach using chaotic species based particle swarm optimization (SPSO) is applied to the multi-template matching (MTM) process. To test its performance, the proposed Chaotic SPSO based MTM algorithm is compared with other approaches by using real captured PCB images. The Chaotic SPSO based MTM method is proven to be superior to other methods in both efficiency and effectiveness.

Evolving Gaits of a Hexapod Robot by Recurrent Neural Networks With Symbiotic Species-Based Particle Swarm Optimization

This paper proposes a new learning approach for evolving dynamic gaits of a hexapod robot. The controller that coordinates the leg movements consists of fully connected recurrent neural networks (FCRNNs). To automate the FCRNN parameter design, a symbiotic species-based particle swarm optimization (SSPSO) algorithm is proposed. There are multiple swarms in the SSPSO, where a swarm only optimizes the relevant parameters to a single node. The number of swarms is equal to the number of nodes in an FCRNN. The symbiotic behavior of particles from different swarms corresponds to the symbiotic structure of different nodes in an FCRNN. For a particle update, particles in different swarms update independently using a local version of particle swarm optimization (PSO) based on speciation. In each swarm, species are formed adaptively in each iteration according to both particle distance and performance. The design of FCRNNs using the SSPSO for temporal sequence generation and hexapod robot dynamic gait evolution for forward movement is conducted. For the latter, a multiple-FCRNN controller is first designed using a simulated hexapod robot. The designed controller is then successfully applied to a real hexapod robot gait control. The SSPSO is compared with the genetic algorithm and different PSO algorithms to verify its efficiency and effectiveness.

Enhanced speciation in particle swarm optimization for multi-modal problems

In this paper, we present a novel multi-modal optimization algorithm for finding multiple local optima in objective function surfaces. We build from Species-based particle swarm optimization (SPSO) by using deterministic sampling to generate new particles during the optimization process, by implementing proximity-based speciation coupled with speciation of isolated particles, and by including “turbulence regions” around already found solutions to prevent unnecessary function evaluations. Instead of using error threshold values, the new algorithm uses the particle’s experience, geometric mean, and “exclusion factor” to detect local optima and stop the algorithm. The performance of each extension is assessed with leave-it-out tests, and the results are discussed. We use the new algorithm called Isolated-Speciation-based particle swarm optimization (ISPSO) and a benchmark algorithm called Niche particle swarm optimization (NichePSO) to solve a six-dimensional rainfall characterization problem for 192 rain gages across the United States. We show why it is important to find multiple local optima for solving this real-world complex problem by discussing its high multi-modality. Solutions found by both algorithms are compared, and we conclude that ISPSO is more reliable than NichePSO at finding optima with a significantly lower objective function value.

Multiple Object Tracking Via Species-Based Particle Swarm Optimization

Multiple object tracking is particularly challenging when many objects with similar appearances occlude one another. Most existing approaches concatenate the states of different objects, view the multi-object tracking as a joint motion estimation problem and search for the best state of the joint motion in a rather high dimensional space. However, this centralized framework suffers from a high computational load. We bring a new view to the tracking problem from a swarm intelligence perspective. In analogy with the foraging behavior of bird flocks, we propose a species-based particle swarm optimization algorithm for multiple object tracking, in which the global swarm is divided into many species according to the number of objects, and each species searches for its object and maintains track of it. The interaction between different objects is modeled as species competition and repulsion, and the occlusion relationship is implicitly deduced from the “power” of each species, which is a function of the image observations. Therefore, our approach decentralizes the joint tracker to a set of individual trackers, each of which tries to maximize its visual evidence. Experimental results demonstrate the efficiency and effectiveness of our method.

Harmonic Minimization in Multilevel Inverters Using Modified Species-Based Particle Swarm Optimization

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> Harmonic minimization in multilevel inverters is a complex optimization problem that involves nonlinear transcendental equations having multiple local minima. In this paper, a solution to the harmonic minimization problem using a novel particle swarm optimization (PSO) approach based on species-based PSO (SPSO) is presented. The original SPSO is modified, which increased the robustness of algorithm to find global optimum of the search space. The proposed method is able to find the optimum switching angles when their number is increased, while it is not possible to determine them using either conventional iterative techniques or resultant theory method. Theoretical results are verified by experiments and simulations for an 11-level H-bridge inverter. Results show that the proposed method effectively minimizes a large number of specific harmonics, and the output voltage results in very low total harmonic distortion and switching frequency. </para>

A particle swarm optimization approach for components placement inspection on printed circuit boards

The importance of the inspection has been magnified by the requirements of the modern manufacturing environment. In electronics mass-production manufacturing facilities, especially in the printed circuit board (PCB) industry, 100% quality assurance of all work-in-process and finished goods is required in order to reduce the scrap costs and re-work rate. One of the challenges for PCB inspection is in the use of a surface mount device (SMD) placement check. Missing, misaligned or wrongly rotated components are the critical causes of defects. To prevent the PCB from having these defects, inspection must be done before the solder reflow process commences, otherwise, everything will be too late. The research reported in this paper concentrates on automatic object searching techniques, in a grey-scale captured image, for locating multiple components on a PCB. The presented approach includes the normalized cross correlation (NCC) based multi-template matching (MTM) method. The searching process has been carried out by using the proposed accelerated species based particle swarm optimization (ASPSO) method and the genetic algorithm (GA) approach as a reference. The experimental results of the ASPSO-based MTM approaches are reported.