Search

Recently, deep learning has been successfully applied to robotic grasp detection. Based on convolutional neural networks (CNNs), there have been lots of end-to-end detection approaches. But end-to-end approaches have strict requirements for the dataset used for training the neural network models and it’s hard to achieve in practical use. Therefore, we proposed a two-stage approach using particle swarm optimizer (PSO) candidate estimator and CNN to detect the most likely grasp. Our approach achieved an accuracy of 92.8% on the Cornell Grasp Dataset, which leaped into the front ranks of the existing approaches and is able to run at real-time speeds. After a small change of the approach, we can predict multiple grasps per object in the meantime so that an object can be grasped in a variety of ways.

Particle swarm optimization (PSO) is an efficient optimization algorithm and has been applied to solve various real-world problems. However, the performance of PSO on a specific problem highly depends on the velocity updating strategy. For a real-world engineering problem, the function landscapes are usually very complex and problem-specific knowledge is sometimes unavailable. To respond to this challenge, we propose a multipopulation ensemble particle swarm optimizer (MPEPSO). The proposed algorithm consists of three existing efficient and simple PSO searching strategies. The particles are divided into four subpopulations including three indicator subpopulations and one reward subpopulation. Particles in the three indicator subpopulations update their velocities by different strategies. During every learning period, the improved function values of the three strategies are recorded. At the end of a learning period, the reward subpopulation is allocated to the best-performed strategy. Therefore, the appropriate PSO searching strategy can have more computational expense. The performance of MPEPSO is evaluated by the CEC 2014 test suite and compared with six other efficient PSO variants. These results suggest that MPEPSO ranks the first among these algorithms. Moreover, MPEPSO is applied to solve four engineering design problems. The results show the advantages of MPEPSO. The MATLAB source codes of MPEPSO are available at https://github.com/zi-ang-liu/MPEPSO.

Generally, the actual explosive is not suitable for the training of security personnel due to its danger. Hence, it is significant to create the simulant as similar as possible to the real explosive, where the difficulties are derived from finding safe compounds from the compound database and their related proportion. In this paper, a cooperative co-evolutionary comprehensive learning particle swarm optimizer is proposed to obtain the formulation design of explosive simulant. To be specific, the proposed algorithm employs particle swarm optimization as the optimizer and creates two cooperative populations focusing on finding compounds and their proportions, respectively. Moreover, a comprehensive cooperative strategy is designed to improve the solution diversity and thus enhance the search performance. To the best of our knowledge, this is the first attempt to employ evolutionary algorithm to design explosive simulant formulation. Comprehensive experiments are conducted on several typical explosives and results demonstrate the superiority of the proposed algorithm in comparison to other algorithms.

In this article, a new algorithm, namely the multi-objective competitive swarm optimizer (MOCSO), is introduced to handle multi-objective problems. The algorithm has been principally motivated from the competitive swarm optimizer (CSO) and the NSGA-II algorithm. In MOCSO, a pair wise competitive scenario is presented to achieve the dominance relationship between two particles in the population. In each pair wise competition, the particle that dominates the other particle is considered the winner and the other is consigned as the loser. The loser particles learn from the respective winner particles in each individual competition. The inspired CSO algorithm does not use any memory to remember the global best or personal best particles, hence, MOCSO does not need any external archive to store elite particles. The experimental results and statistical tests confirm the superiority of MOCSO over several state-of-the-art multi-objective algorithms in solving benchmark problems.

Estimating the location of a target is essential for many applications such as asset tracking, navigation, and data communications. Time-difference-of-arrival (TDOA) based localization has the main advantage that it does not require synchronization between the transmitting and the receiving sides. Phase-difference-of-arrival (PDOA) provides additional information that can be leveraged to enhance localization performance. The combination of TDOA and PDOA for localization has not been reported in the literature. In this letter, we propose a novel approach that incorporates both TDOA and PDOA to achieve improved position estimation. In the proposed approach, an initial location estimate is obtained by optimizing a TDOA cost function. Next, a PDOA, or a hybrid TDOA-PDOA cost function is optimized using a particle swarm optimizer to obtain the final location estimate. Simulation results show that the proposed approach sufficiently, and justifiably, improves localization performance relative to pure TDOA methods.

This paper proposes a novel binary particle swarm optimization algorithm (called IRBPSO) to address high-dimensional knapsack problems in dynamic environments (DKPs). The IRBPSO integrates an immune-based response strategy into the basic binary particle swarm optimization algorithm for improving the quantity of evolutional particles in high-dimensional decision space. In order to enhance the convergence speed of the IRBPSO in the current environment, the particles with high fitness values are cloned and mutated. In addition, an external archive is designed to store the elite from the current generation. To maintain the diversity of elites in the external archive, the elite of current generation will replace the worst one in the external archive if and only if it differs from any of the existing particles in the external archive based on the Hamming distance measurement when the archive is due to update. In this way, the external archive can store diversiform elites for previous environments as much as possible, and so as to the stored elites are utilized to transfer historical information to new environment for assisting to solve the new optimization problem. Moreover, the environmental reaction scheme is also investigated in order to improve the ability of adapting to different kinds of dynamic environments. Experimental results on a series of DKPs with different randomly generated data sets indicate that the IRBPSO can faster track the changing environments and manifest superior statistical performance, when compared with peer optimization algorithms.

In this work, the Modified competitive swarm optimizer (MCSO) is applied for solving the economic dispatch problem. This algorithm got inspiration from particle swarm optimizer (PSO) algori...

Quantum inspired particle swarm optimization (QPSO) stimulated by perceptions from particle swarm optimization and quantum mechanics is a stochastic optimization method. Although, it has shown good performance in finding the optimal solution to many electromagnetic problems. However, sometimes it falls to local optima when dealing with hard optimization problems. Thus, to preserve a good balance between local and global searches to avoid premature convergence in quantum particle swarm optimization, this paper proposed three enhancements to the original QPSO method, the proposed method is called modified quantum particle swarm optimization (MQPSO) algorithm. Firstly, a novel selection technique is introduced that will choose the best particle among the population within the search domain to achieve a high-performance exploration. Secondly, a new mutation method is used to preserve the easiness of available QPSOs. Also, a dynamic parameter strategy is proposed for further facilitating the algorithm and tradeoff between exploration and exploitation searches. The experimental results obtained by solving standard benchmark functions and an electromagnetic design problem which is the superconducting magnetic energy storage (SMES) system available in both three parameters and eight parameters problems are reported to showcase the usefulness of the proposed approach.

Chaotic response in the permanent magnet synchronous motor (PMSM) is an undesirable performance that may affect motor stability. This unexpected behavior occurs due to the disruption in the system parameters and load disturbance. To control this unwanted performance, it’s essential to introduce a robust method to determine the PMSM model parameters efficiently and expeditiously. Two PMSM models depending on the type of its nonlinear differential equations are investigated. The first one is the integer-order PMSM model, while the other is the fractional-order model. In this work, novel developed optimization variants called chaotic heterogeneous comprehensive learning particle swarm optimizer (CHCLPSO) is proposed. In CHCLPSO, the standard heterogeneous comprehensive learning particle swarm optimizer (HCLPSO) is cooperated into ten different chaos maps to adjust some of its parameters. Six CHCLPSO variables are introduced in addition to the standard HCLPSO version to estimate the parameters of the integer-order and the fractional-order PMSM models that are corresponding to the chaotic behavior. A comparison among the results of the introduced variants and the original algorithm is carried out. Moreover, a comprehensive comparison with other recent algorithms is performed. The primary outcome proves that the chaos maps have a remarkable influence in both of the consistency and the accuracy of the results of the HCLPSO with less execution time over the integer and the fractional-order models, especially CHCLPSO-III and CHCLPSO-V with sinusoidal and piecewise maps, respectively.

Due to the lack of global search capacity, most evolutionary or swarm intelligence based algorithms show their inefficiency when optimizing multi-modal problems. In this paper, we propose a grouping particle swarm optimizer (GPSO) to solve this kind of problem. In the proposed algorithm, the swarm consists of several groups. For every several iterations, an elite group is constructed and used to replace the worst one. The thought of grouping is helpful for improving the diversity of the solutions, and then enhancing the global search ability of the algorithm. In addition, we apply a simple mutation operator to the best solution so as to help it escape from local optima. The GPSO is compared with several variants of particle swarm optimizer (PSO) and some state-of-the-art evolutionary algorithms on CEC15 benchmark functions and three practical engineering problems. As demonstrated by the experimental results, the proposed GPSO outperforms its competitors in most cases.

Photovoltaic modeling has attracted researchers’ attention worldwide because of its importance in the photovoltaic system design. Therefore, several photovoltaic models have been introduced as static and dynamic photovoltaic models. Moreover, a novel fractional order dynamic photovoltaic model has been recently developed to enhance the accuracy and flexibility of the conventional integral order one. The unknown parameters of these models should be extracted accurately to achieve a proper photovoltaic system design and operation. In this work, novel Chaotic Heterogeneous Comprehensive Learning Particle Swarm Optimizer variants are introduced, where the Heterogeneous Comprehensive Learning Particle Swarm Optimizer is combined with ten different chaos maps to adapt its parameters. Six Chaotic Heterogeneous Comprehensive Learning Particle Swarm Optimizer variants are proposed in addition to the standard Heterogeneous Comprehensive Learning Particle Swarm Optimizer version to identify the parameters of both the static and the dynamic models based on different experimental datasets. To demonstrate the superiority of the developed variants, their results are compared to the most recent state-of-the-art algorithms with the aid of statistical analysis methods. The main outcome is that, in both of the static and the dynamic photovoltaic models, the Chaotic Heterogeneous Comprehensive Learning Particle Swarm Optimizer variants show their efficiency, accuracy and robustness not only over Heterogeneous Comprehensive Learning Particle Swarm Optimizer but also over recently published algorithms. They provide better fitting relative to the experimental datasets with the least deviation error and the fastest convergence speed as well. In the case of static models, the fourth variant of Chaotic Heterogeneous Comprehensive Learning Particle Swarm Optimizer with an iterative map for the single diode model, the third variant of Chaotic Heterogeneous Comprehensive Learning Particle Swarm Optimizer with singer map for the double diode model of solar cell. On the other hand, for the dynamic models, the second Chaotic Heterogeneous Comprehensive Learning Particle Swarm Optimizer variant with sinusoidal map for the integral order dynamic photovoltaic model and the sixth variant of Chaotic Heterogeneous Comprehensive Learning Particle Swarm Optimizer with Gauss/mouse map for the fractional order dynamic photovoltaic model offer the best performance.

Power system cybersecurity have recently become important due to cyber-attacks. Due to Advanced computer science and machine learning (ML) applications have being used by malicious attackers; cybersecurity is more crucial to create sustainable, reliable, efficient and well-protected cyber-systems. Considering the fact that it is required for power system operators to pay more attention to develop sophisticated detection mechanism. In this study, a novel machine learning based detection algorithm that combines five most popular ML algorithms with Particle Swarm Optimizer (PSO) is developed and tested by using intelligent hacking algorithm that is specially developed to measure of effectives of this study. The hacking algorithm provides three different types of injections: random, continuous random and slowly injections by adaptive manner, so that detection would be harder. Results shows that recall values with proposed algorithm for each different type of attacks have been increased.

Diabetes has become a major health concern among people around the world. It may reduce the life span, and enhances the probability of various kind of cardiovascular diseases. Prediction of diabetes may provide an alarm to the people who are required to check their health status. It is a very challenging task as medical data is very much saturated and complex. In this work, we have predicted diabetes from several lifestyle parameters such as BMI, age, pregnancy and symptoms such as weight loss, visual blurring, weight loss, and so on. Several machine learning algorithms were used to predict diabetes. These machine learning algorithms were further optimized using Particle Swarm Optimizer and Social Group Optimizer. Social Group Optimized Gradient Boosted Classifier (GBC) achieved an accuracy of 71.85% in predicting diabetes from lifestyle parameters. The proposed architecture achieved 98.26% accuracy in case of prediction from symptoms using Social Group Optimized Random Forest Algorithm.

Optimization is a buzzword, whenever researchers think of engineering problems. This paper presents a new metaheuristic named dingo optimizer (DOX) which is motivated by the behavior of dingo (Canis familiaris dingo). The overall concept is to develop this method involving the collaborative and social behavior of dingoes. The developed algorithm is based on the hunting behavior of dingoes that includes exploration, encircling, and exploitation. All the above prey hunting steps are modeled mathematically and are implemented in the simulator to test the performance of the proposed algorithm. Comparative analyses are drawn among the proposed approach and grey wolf optimizer (GWO) and particle swarm optimizer (PSO). Some of the well-known test functions are used for the comparative study of this work. The results reveal that the dingo optimizer performed significantly better than other nature-inspired algorithms.

A new approach to node localization is proposed by using the combined localization model in Wireless sensor nets (WSN) with a hybridized swarm evolution algorithm (HSEA). Mathematically, based on the distances of the localizing model and Pareto, the fitness function is determined. To achieve better efficiency, the HSEA is implemented by combining the particle swarm optimizer (PSO) with the differential-evolution (DE) algorithms. Applied HSEA deals with the node location situation in WSN. The simulation compares the results of the proposed scheme with the others schemes to demonstrate that the suggested task using HSEA achieves better performance than the competitors.

Particle swarm optimization (PSO) attracts much attention due to its ability in solving complex practical engineering problems effectively. To further improve its performance, a heterogeneous particle swarm optimizer (HPSO) is proposed in this work. Five widely used benchmark functions are selected to test its efficiency. Furthermore, five state-of-the-art improved PSO variants are selected for a comparisons purpose. The results demonstrate that HPSO is better than the other five algorithms. A logistics problem in aircraft manufacturing is then studied and solved. The results show HPSO's superiority over its tested PSO variants.

The Bare Bones Particle Swarm Optimization (BBPSO), because of its implementation simplicity, has been a popular swarm-based metaheuristic algorithm for solving optimization problems. However, as found in its many variants, their search behaviors were not considered in the design. Instead of employing heuristics, we formulate a low complexity particle swarm optimizer, called the First-Order Bare Bones Particle Swarm Optimizer (FODBB), whose behavior obeys the principle of first-order difference equations. The search trajectory can be constructed in a prescribed manner together with decreasing random searches that enable particles to explore the search space more completely. This characteristic thus allows for a wider search coverage at initial iterations and consequently improves the search performance. A comparative evaluation with recently reported BBPSO algorithms was conducted and experimental results indicate that the proposed optimizer outperforms others in a majority of benchmark optimization functions.

Particle swarm optimizer was proposed in 1995, and since then, it has become an extremely popular swarm intelligent algorithm with widespread applications. Many modified versions of it have been developed, in which, comprehensive learning particle swarm optimizer is a very powerful one. In order to enhance its performance further, a local search based on Latin hypercube sampling is combined with it in this work. Due to that a hypercube should become smaller and smaller for better local search ability during the search process, a control method is designed to set the size of the hypercube. Via numerical experiments, it can be observed that the comprehensive learning particle swarm optimizer with the local search based on Latin hypercube sampling has a strong ability on both global and local search. The hybrid algorithm is applied in cylindricity error evaluation problem and it outperforms several other algorithms. Published version This research work is supported in part by National Natural Science Foundation of China (NSFC) under Grant No. 61603145, in part by Natural Science Foundation of Hubei Province under Grant No. 2015CFB528, and in part by Open Project of State Key Lab of Digital Manufacturing Equipment & Technology under Grant DMETKF2018011.

The Particle Swarm Optimization (PSO) algorithm is an attractive meta-heuristic approach for difficult optimization problems. It is able to produce satisfactory results when classical analytic methods cannot be applied. However, the design of PSO was usually based on ad-hoc attempts and its behavior could not be exactly specified. In this work, we propose to drive particle into oscillatory trajectories such that the search space can be covered more completely. A difference equation based analysis is conducted to reveal conditions that guarantee trajectory oscillation and solution convergence. The settings of cognitive and social learning factors and the inertia weight are then determined. In addition, a new strategy in directing these parameters to follow a linearly decreasing profile with a perturbation is formulated. Experiments on function optimizations are conducted and compared to currently available methods. Results have confirmed that the proposed Oscillatory Particle Swarm Optimizer (OSC-PSO) outperforms other recent PSO algorithms using adaptive inertia weights.

This paper presents a novel global maximum power point tracking (MPPT) method based on the sorting particle swarm optimizer (PSO). At first, the output characteristics of photovoltaic (PV) array un...

The organic Rankine cycle (ORC) system powered by exhaust heat has great potential in improving engine performance. Many optimizations of the only ORC system were conducted, while the existing literature pays limited attention to the optimization of the engine–ORC combined system. By considering the importance of interaction, cooperation, and influence between the engine and ORC system, a global optimization of the diesel engine–ORC combined system (herein, the combined system) is conducted in this paper with respect to power output and fuel economy. A GT-Suite model of the combined system and a GT-Suite/Simulink co-simulation model are proposed to obtain the optimum operating parameters of the engine and the ORC system under various operating conditions. Furthermore, the effects of the operating parameters, namely, exhaust valve timing, injection timing, expander speed, and pump speed, are evaluated on the combined system. In addition, models of the engine and the ORC system are calibrated, and a particle swarm optimizer (PSO) is designed and adopted for global optimization. Optimization results show improvements of 3.24% and 3.13% on the power output and brake specific fuel consumption (BSFC), respectively, with full engine load when the engine is operated at 3600 r/min. In the optimization of fuel economy with partial engine load, a maximum reduction of 5.71% on the BSFC of the combined system is obtained at 3600 r/min engine speed.

A particle swarm optimization algorithm with crossover operation (PSOCO) is proposed. In the proposed PSOCO, two different crossover operations are employed in order to breed promising exemplars. By performing crossover on the personal historical best position of each particle, the effective guiding exemplars are constructed and they maintain a good diversity. In turn, these high quality exemplars are used to guide the evolution of particles. PSOCO is two-layer particle swarm optimization with positive feedback mechanism. In order to test the performance of PSOCO, we use a set of widely used benchmark functions. The experimental results demonstrate that the proposed PSOCO is a competitive optimizer in terms of both solution quality and efficiency.

Purpose The aim of this paper is to explore the potential of standard quantum particle swarm optimization algorithms to solve single objective electromagnetic optimization problems. Design/methodology/approach A modified quantum particle swarm optimization (MQPSO) algorithm is designed. Findings The MQPSO algorithm is an efficient and robust global optimizer for optimizing electromagnetic design problems. The numerical results as reported have demonstrated that the proposed approach can find better final optimal solution at an initial stage of the iterating process as compared to other tested stochastic methods. It also demonstrates that the proposed method can produce better outcomes by using almost the same computation cost (number of iterations). Thus, the merits or advantages of the proposed MQPSO method in terms of both solution quality (objective function values) and convergence speed (number of iterations) are validated. Originality/value The improvements include the design of a new position updating formula, the introduction of a new selection method (tournament selection strategy) and the proposal of an updating parameter rule.

Item classification is an issue among inventory managers who want to achieve key-point management of items with different emphases, thus prompting managers and researchers to pursue efficient and effective classification algorithms. In processing multiple-criteria inventory classifications, back-propagation neural network (BPNN) shows its superiority in balancing items’ multiple competing attributes. However, because the training processes and the final results of BPNN rely on the initial connection weights and thresholds, finding reasonable values of the two parameters is a challenge. This paper introduces biogeography-based optimization (BBO) and an adaptive mutation particle swarm optimizer (AMPSO) to BPNN to optimize the training parameters, i.e., the global initial connection weights and thresholds, of BPNN. On the basis of this, two hybrid classification algorithms—BBO-BPNN and AMPSO-BPNN—are presented. Real-life data from three cases are adopted to verify the effectiveness and feasibility of the two proposed hybrid algorithms. Experimental results demonstrate that BBO-BPNN and AMPSO-BPNN show higher classification accuracy than other hybrid models, i.e., BPNN, PSO-BPNN, DE-BPNN, and GA-BPNN, in the multiple-criteria inventory classification problem.

This paper presents a novel meta-heuristic algorithm, dynamic particle swarm optimizer with escaping prey (DPSOEP), for solving constrained non-convex and piecewise optimization problems. In DPSOEP, the particles developed from two different species are classified into three different types, consisting of preys, strong particles and weak particles, to simulate the behavior of hunting and escaping characteristics observed in nature. Compared to other variants of particle swarm optimizer (PSO), the proposed algorithm takes account of an escaping mechanism for the preys to circumvent the problem of local optimum and also develops a classification mechanism to cope with different situations in the search space so as to achieve a good balance between its global exploration and local exploitation abilities. Simulation results obtained based on thirteen benchmark functions and two practical economic dispatch problems prove the effectiveness and applicability of the DPSOEP to deal with non-convex and piecewise optimization problem, considering the integration of linear equality and inequality constraints.

It has been shown that swarm topologies influence the behavior of Particle Swarm Optimization (PSO). A large number of connections stimulates exploitation, while a low number of connections stimulates exploration. Furthermore, a topology with four links per particle is known to improve PSO’s performance. In spite of this, there are few studies about the influence of swarm topologies in Multi-Objective Particle Swarm Optimizers (MOPSOs). We analyze the influence of star, tree, lattice, ring and wheel topologies in the performance of the Speed-constrained Multi-objective Particle Swarm Optimizer (SMPSO) when adopting a variety of multi-objective problems, including the well-known ZDT, DTLZ and WFG test suites. Our results indicate that the selection of the proper topology does indeed improve the performance in SMPSO.

Most multiobjective particle swarm optimizers (MOPSOs) often face the challenges of keeping diversity and achieving convergence on tackling many-objective optimization problems (MaOPs), as they usually use the nondominated sorting method or decomposition-based method to select the local or best particles, which is not so effective in high-dimensional objective space. To better solve MaOPs, this paper presents a novel angular-guided particle swarm optimizer (called AGPSO). A novel velocity update strategy is designed in AGPSO, which aims to enhance the search intensity around the particles selected based on their angular distances. Using an external archive, the local best particles are selected from the surrounding particles with the best convergence, while the global best particles are chosen from the top 20% particles with the better convergence among the entire particle swarm. Moreover, an angular-guided archive update strategy is proposed in AGPSO, which maintains a consistent population with balanceable convergence and diversity. To evaluate the performance of AGPSO, the WFG and MaF test suites with 5 to 10 objectives are adopted. The experimental results indicate that AGPSO shows the superior performance over four current MOPSOs (SMPSO, dMOPSO, NMPSO, and MaPSO) and four competitive evolutionary algorithms (VaEA, θ-DEA, MOEA\D-DD, and SPEA2-SDE), when solving most of the test problems used.

Multi-objective optimization deals with problems having two or more conflicting objectives that have to be optimized simulta-neously. When the objectives change somehow with time, the problems become dynamic, and if the decision maker indicates preferences at runtime, then the algorithms to solve them become interactive. In this paper, we propose the integration of SMPSO/RP, an interactive multi-objective particle swarm optimizer based on SMPSO, with InDM2, an algorithmic template for dynamic interactive optimization with metaheuristics. The result is SMPSO/RPD, an algorithm that provides the search capabilities of SMPSO, incorporates an interactive preference articulation mechanism based on defining one or more reference points, and is able to deal with dynamic problems. We conduct a qualitative study showing the working of SMPSO/RPD on three benchmark problems, remaining a qualitative analysis as an open line of future research. Ministerio de Educación y Ciencia TIN2017-86049-R

Conventional pseudo-random testing in Built-InSelf-Test (BIST) usually requires a huge amount of testing time. This issue can be addressed with a Weighted Random Pattern generation that can produce test patterns in order to achieve high fault coverage with the fewer number of test vectors. Determining such input weights for a particular circuit is an NP-hard problem. In this paper, we have proposed a technique to converge to a high-quality input weight vector using a Particle Swarm Optimizer (PSO) with the help of a Deep Neural Network (DNN). The DNN prediction of fault coverage value as well as the parallel training of the DNN along with the evolution of the PSO makes this significantly fast. The technique has been tested with ISCAS'85 and ISCAS'89 benchmark circuits. The result shows that the DNN gets capable of predicting the fault coverage values accurately for weight assignments suggested by the particles in PSO. Also, it is observed that the proposed approach is very efficient in covering a large number of faults with less test vectors in self-testing circuits.

A Bi-swarm Particle Swarm Optimizer with novel neighborhood topology strategy (BPSO-NT) is proposed in this paper. The increase of its population diversity helps to improve its global search ability. The strategy of updating neighborhood topology that plays a vital role in the particle swarm optimization algorithm (PSO) is studied by leveraging link prediction techniques. Different learning strategies are utilized to update the velocity of individuals in the two swarms of BPSO-NT. From comparison results with the state-of-the-art PSO variants on ten benchmark functions, the superiority of the proposed algorithm is demonstrated. Furthermore, BPSO-NT is applied to the intermodal transportation planning, and statistical results show that BPSO-NT outperforms other PSO variants in this practical optimization problem.

This paper presents a dynamic multichain particle swarm optimization (DMCPSO) algorithm to solve the security-constrained optimal power flow (SCOPF) problem, which aims to minimize the predefined cost while taking both system capacity requirements and operating security constraints into account. The DMCPSO is based on a dynamic multichain topology and an adaptive parameter control mechanism. The dynamic multichain topology organizes the swarm population in the chain structure at both the individual and subpopulation levels. Different particles can play different roles in this topology to enhance both exploration and exploitation via local and global communication mechanisms. Moreover, to boost the benefits brought by this topology, three types of parameters, i.e., the inertia weight w, scaling factor F, and mutation probability $p$ are controlled in an adaptive manner to further boost exploration and exploitation. Numerical results demonstrate the superior performance of the proposed DMCPSO over three reference PSO algorithms in the literature.

This paper presents a hybrid multiswarm particle swarm optimization (HMPSO) algorithm to solve the security-constrained optimal power flow (SCOPF) problem, which aims to minimize the predefined cost while taking both system capacity requirement and operating security constraints into account. The HMPSO is based on the comprehensive learning (CL) strategy and is hybridized with the simulated annealing (SA) algorithm. The swarm population is divided into multiple subpopulations. One of the subpopulations is assigned for exploitation and the rest subpopulations are responsible for exploration. The CL strategy is used to create the exemplars for all subpopulations. By combining with the SA, the HMPSO builds an asymmetrical bidirectional transmission channel to exchange information between the explorative subpopulations and the exploitative subpopulation, which can enhance both the exploration and exploitation. Numerical results demonstrate the superior performance of the proposed HMPSO over three reference PSO algorithms in the literature.

As a global-optimized and naturally inspired algorithm, particle swarm optimization (PSO) is characterized by its high quality and easy application in practical optimization problems. However, PSO has some obvious drawbacks, such as early convergence and slow convergence speed. Therefore, we introduced some appropriate improvements to PSO and proposed a novel chaotic PSO variant with arctangent acceleration coefficient (CPSO-AT). A total of 10 numerical optimization functions were employed to test the performance of the proposed CPSO-AT algorithm. Extensive contrast experiments were conducted to verify the effectiveness of the proposed methodology. The experimental results showed that the proposed CPSO-AT algorithm converges quickly and has better stability in numerical optimization problems compared with other PSO variants and other kinds of well-known optimal algorithms.

International audience; An innovative paradigm for the synthesis of feasible sources radiating arbitrary field distributions is introduced. Towards this end, the design problem is formulated in terms of an inverse scattering (IS) one aimed at determining the electromagnetic (EM) properties of an isotropic/non-magnetic field-manipulating device able to modify the radiation features of an assigned source such that it is able to match a desired target field distribution in a given domain. An illustrative numerical example is provided to preliminarily assess the proposed innovative antenna design framework.

Diversity maintenance is crucial for particle swarm optimizer&rsquo, s (PSO) performance. However, the update mechanism for particles in the conventional PSO is poor in the performance of diversity maintenance, which usually results in a premature convergence or a stagnation of exploration in the searching space. To help particle swarm optimization enhance the ability in diversity maintenance, many works have proposed to adjust the distances among particles. However, such operators will result in a situation where the diversity maintenance and fitness evaluation are conducted in the same distance-based space. Therefore, it also brings a new challenge in trade-off between convergence speed and diversity preserving. In this paper, a novel PSO is proposed that employs competitive strategy and entropy measurement to manage convergence operator and diversity maintenance respectively. The proposed algorithm was applied to the large-scale optimization benchmark suite on CEC 2013 and the results demonstrate the proposed algorithm is feasible and competitive to address large scale optimization problems.

Description of the states in both before updating and after updating.Figure gives an example to illustrate our algorithm. Part (a) gives the state of before updating. Part (b) explains the state of after updating. Assume there are five peaks in the search space, numbered 1-5 in Part (b). However, only four peaks are detected by the current population, numbered 1-4 in Part (a).Where, ź denotes a replacer from the memory, Δ denotes a new generated particle around the replacer, and ź denotes a replaced particle in the population. We can see that no more than one particle is replaced in each sub-population.Since ź in peak 5 is better than the closest seed, i.e. the seed in sub-population 2, and its distance to the closest seed is larger than rs, the sub-population 5 is created to exploit this area.In sub-population 3, ź is better than the closest seed, i.e. the seed in sub-population 3, and the distance between it to the closest seed is larger than 0.5×rs and less than rs, so only one Δ is created around the ź.In sub-population 2, ź is better than the closest seed, i.e. the seed in sub-population 2, but the distance between it to the closest seed is less than 0.5×rs, so only ź is added to this sub-population.There is one case that has not been described in this example. Here we suppose the replacer is not better than the worst particle in sub-population 1, so no replacement is conducted in this sub-population.Display Omitted A new population updating method is proposed to enhance a representative algorithm, i.e. the Species-based Particle Swarm Optimization.Experimental results show that the MSPSO is competitive on MPB, CMPB and DRGDB.The effect of the memory size on the performance of the proposed algorithm is tested. 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.

Facing energy shortage and severe environmental pollution, manufacturing companies need to urgently energy consumption, make rational use of resources and improve economic benefits. This paper formulates a multi-objective optimization model for lathe turning operations which aims to simultaneously minimize energy consumption, machining cost and cutting time. A dynamic multi-swarm particle swarm optimizer (DMS-PSO) is proposed to solve the formulation. A case study is provided to illustrate the effectiveness of the proposed algorithm. The results show that the DMS-PSO approach can ensure good convergence and diversity of the solution set. Additionally, the optimal machining parameters are identified by fuzzy comprehensive evaluation (FCE) and compared with empirical parameters. It is discovered that the optimal parameters obtained from the proposed algorithm outperform the empirical parameters in all three objectives. The research findings shed new light on energy conservation of machining operations.

Multipopulation methods, which can enhance the population diversity, are well suited for dynamic optimization. However, there are still some challenges need to be tackled when multipopulation methods are employed, namely, how to avoid sensitive parameters when creating sub-populations, and how to effectively adapt to the changing optima continuously during the search process. Therefore, a novel multipopulation algorithm based on the affinity propagation clustering is proposed to address the above challenges. In the proposed method, affinity propagation clustering is applied for automatically creating sub-populations by message-passing process, which can avoid some extra parameters. Moreover, a simple but effective strategy, denoted as optimal particles relocation, is proposed for responding to environmental changes. In this strategy, the best particles in each sub-population are first stored in a memory. Then, local search is applied for helping the memory to quickly locate new peaks, if the environmental change has occurred. To validate the performance of the proposed algorithm, a variety of experiments have been conducted. The experimental results have demonstrated that the proposed algorithm performs robustly and competitively under different environments.

As a kind of evolutionary algorithms, particle swarm optimization is famous for its simplicity and efficiency in optimization. However, for complex problems, PSO is prone to be trapped into the local optima. To address this issue, a particle swarm optimizer with niching strategy and entropy-based exploration strategy (PSO-NE) is proposed in this paper. To be specific, on one hand, a distance based niching strategy and the competitive learning strategy are adopted to design the exploitation in PSO-NE; on the other hand, the exploration in PSO-NE is achieved by an entropy based exploring strategy. With such kind of designs, the exploitation and exploration in PSO-NE can be dependently adjusted, which is beneficial for balancing these two factors. To validate the effectiveness of the proposed algorithm, extensive experiments have been conducted based on 28 benchmarks from CEC’ 2013. The proposed algorithm shows its competitive performance with comparing to six other typical variants of PSO.

This study introduces reverse direction supported particle swarm optimization (RDS-PSO) with an adaptive regulation procedure. It benefits from identifying the global worst and global best particles to increase the diversity of the PSO. The velocity update equation of the original PSO was changed according to this idea. To control the impacts of the global best and global worst particles on the velocity update equation, the alpha parameter was added to the velocity update equation. Moreover, a procedure for diversity regulation based on cosine amplitude or max–min methods was introduced. Alpha value was changed adaptively with respect to this diversity measure. Besides, RDS-PSO was implemented with both linearly increasing and decreasing inertia weight (with 1,000 and 2,000 iterations) in order to survey the effects of these variations on RDS-PSO performances. Six most commonly used benchmark functions and three medical classification problems were selected as experimental data sets. All experimental results showed that when the grain searching ability is not so small in the last generations, the algorithm performance continues to increase. Experimental proof of it was showed up especially in RDS-PSO using the cosine amplitude approach. Because the best results among all the RDS-PSO types for decreasing inertia weight modes were obtained with 2,000 maximal iterations rather than 1,000 ones. © 2018 John Wiley & Sons, Ltd

Mismatch power loss is the unfortunate problem created as a result of the partial shading phenomenon. To relieve the entire photovoltaic system from this issue, reconfiguring the photovoltaic modules of the considered photovoltaic array is proposed as a favorable solution to disperse the shadow regularly. Therefore, in the current work, a novel developed optimization algorithm named modified harris hawks optimizer is introduced aiming to provide the optimal reconfiguration pattern of the switching matrix to maximize the generated power from the array. The basic version of the harris hawks optimizer is implemented for the same problem over several stages of the analysis, as well. Rearrangements of four patterns of shadow (short broad, long broad, short narrow, and long narrow patterns) are implemented on 9 × 9 photovoltaic array. Moreover, the algorithm is also validated on another two shade patterns with 6 × 4 and 6 × 20 photovoltaic arrays are reported in the simulation part. The results of the proposed techniques are compared to those obtained by a total-cross-tied, competence square, particle swarm optimizer, and genetic-based reconfiguration techniques based on several metrics. The utilized measures are mismatch power, fill factor, percentage power loss, as well as percentage power enhancement. The analysis reveals that the introduced approach enhances the produced power by 33.274%, 27.79%, 6.99%, 7.197% over the first four shaded patterns, respectively and increases the generated power by 20%for the other shade patters. Further, the modified harris hawks optimizer proves its excellency in providing the optimal photovoltaic array configuration in less than 1 s. For intensive justification, the modified harris hawks optimizer reconfiguration approach is validated based on an experimental work for 9 × 9 photovoltaic array with twelve shade patterns during a day from 8 ante meridiem to 7 post meridiem with one hour step. Comparing the produced power based on the modified harris hawks optimizer arrangements to those of total-cross-tied and harris hawks optimizer schemes over the daily hours proves the superiority of the modified harris hawks optimizer interconnected system in generating the highest values of power with a uniform dispersion for the shadow and smooth characteristics with saving the energy by 12.569% compared with that produced by total-cross-tied across the day hours.

Multi-objective particle swarm optimization algorithms have been widely used to solve multi-objective optimization problem. This paper presents a novel multi-objective particle swarm optimizer based on simulated annealing and decomposition for solving multi-objective optimization problems. First, the decomposition mechanism is adopted that simplifies a multi-objective optimization problem into a number of scalar single optimization sub-problems and optimizes them simultaneously. And then the simulated annealing strategy is incorporated into the algorithm as a local search operator to improve the search performance of PSO. At last simulation results compared with three classical multi-objective methods using thirteen test functions and one metric taken from the standard literature on multi-objective optimization indicates that the proposed algorithm is effective and competitive.

Piecewise-linear particle swarm optimizer (PPSO) is one of swarm-based optimization algorithms. Although PPSO is a deterministic particle swarm optimization (D-PSO) algorithm, its performance is better than the classical PSO's performance in solving various optimization problems. In particular, PPSO is effective in solving non-separable problems such as rotated problems. In our previous study, we have investigated that PPSO particles can move not only in a direction parallel to the coordinate axes, but also in various directions. We therefore assumed that PPSO particles do not depend on reference frame. In this paper, we analyzed that PPSO algorithm is reference frame invariant (i.e., scaling, translation, and rotational invariants) theoretically, and in order to study effectiveness of the theoretical analysis, we performed numerical experiments and clarified whether PPSO algorithm is reference frame invariant or not.