Composite particle optimization with hyper-reflection scheme in dynamic environments

Modified Differential Evolution with Locality induced Genetic Operators for dynamic optimization

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.

Particle Swarm optimization (PSO) is a search method inspired from the social behaviors of animals. PSO has been found to outperform other methods in various tasks. Area Extended PSO (AEPSO) is an enhanced version of PSO that achieves better performance by balancing its essential intelligent behaviours more intelligently. AEPSO incorporates knowledge with the aim of choosing proper behaviors in each situation. This study provides a comparison between the variations of Basic PSO and AEPSO aiming to address dynamic and time dependent constraint problems in simulated robotic search. The problem is set up in a multi-robot learning scenario. The scenario is based on the use of a team of simulated robots (hereafter referred to as agents) who participate in survivor rescuing missions. The experiments are classified into three simulations. At first, agents employ variations of basic PSO as their decision maker and movement controllers. The first simulation investigates the impacts of swarm size, parameter adjustment, and population density on agents’ performance. Later, AEPSO is employed to improve the performance of the swarm in the same simulations. The final simulation investigates the feasibility of AEPSO in time-dependent, dynamic and uncertain environments. As shown by the results, AEPSO achieves an appreciable level of performance in dynamic, time-dependence and uncertain simulated environments and outperforms the variations of basic PSO, Linear Search and Random Search used in the simulations.

Recently, genetic algorithms (GAs) have been applied to multi-modal dynamic optimization (MDO). In this kind of optimization, an algorithm is required not only to find the multiple optimal solutions but also to locate a dynamically changing optimum. Our fuzzy genetic sharing (FGS) approach is based on a novel genetic algorithm with dynamic niche sharing (GADNS). FGS finds the optimal solutions, while maintaining the diversity of the population. For this, FGS uses several strategies. First, an unsupervised fuzzy clustering method is used to track multiple optima and perform GADNS. Second, a modified tournament selection is used to control selection pressure. Third, a novel mutation with an adaptive mutation rate is used to locate unexplored search areas. The effectiveness of FGS in dynamic environments is demonstrated using the generalized dynamic benchmark generator (GDBG).

Evolutionary computation algorithms are in recent trends for feature selection due to their efficient results as compared to traditional algorithms. The performance of the evolutionary computational algorithm completely depends upon the parameter (like population, number of generations) setting. Existing parameter setting techniques have been developed for specific algorithms. This paper designs a generic pre-processing technique that removes the redundant as well as irrelevant features from the initial given feature set. This work uses Kendall tau to find the non-redundant feature and the Kruskal Wallis for the relevant features. If Kendall tau is not able to select any non-redundant feature then mutual information gain is used to select significant features followed by the Kruskal Wallis to select relevant features. The designed technique has been analyzed on 4 evolutionary computation algorithms, Modified Binary Particle Swarm Optimization, Non-dominated Sorting Genetic Algorithm, Binary Grey Wolf Optimization, and Binary Whale Optimization Algorithm over 6 datasets by using accuracy, the number of features in selected subset, sensitivity, and specificity. The present technique improves the performance of each algorithm in terms of accuracy, sensitivity, and specificity by 1 % on average with a 10 % reduction in the selected feature subset proves the significance of the proposed technique.
 HIGHLIGHTS
 
 This work removes the redundant features from the initial population using Kendall’s tau and mutual information gain to reduce the initial population for any Evolutionary Computation (EC) algorithm
 The relevant features that are highly correlated with class are added back to the initial population to maintain the optimal initial population
 The performance validation has been done using four EC algorithms to show the significance of the work
 
 GRAPHICAL ABSTRACT

Composite materials were made from two or more constituent materials with different chemical or physical properties and have been demonstrated an enhanced performance. It has been widely applied in many fields, such as military hardware, aviation, architecture and fitness equipment. In this work, composite latex particles and block copolymers were synthesized via miniemulsion polymerization. The reaction mechanisms and related applications were investigated. This research was divided into three parts. The objective of the first part was to prepare PS/Fe3O4 composite latex particles by oil in water (O/W) miniemulsion polymerization. The objective of the second part was to prepare P(AA-SA)/ZnO composite latex particles by water in oil (W/O) miniemulsion polymerization. The objective of the third part was to synthesize PMMA-b-PBA block copolymers via controlled/living radical polymerization. In the first part, there were two synthesis pathways to prepare PS/Fe3O4 nano composite latex particles. The first pathway contained a two-step process. First, the Fe3O4 nanoparticles were prepared by a coprecipitation method followed by the surface treatment with lauric acid. Hence, the surface modified Fe3O4 was hydrophobic in nature. In the second step, the monomer droplets containing Fe3O4 and costabilizer were dispersed in water with surfactant by ultrasonication. As the miniemulsion polymerization was initiated by water-soluble initiator, potassium persulfate (KPS), or oil-soluble initiator, 2,2'-azobisisobutyronitrile (AIBN), magnetic composite latex particles could be obtained. The influences of initial formulation on the monomer conversion, size distributions of monomer droplets and latex particles, nucleation mechanism, and morphology of composite particles were investigated in depth. Another synthesis pathway contained a three-step process. On the first step, the Fe3O4 dispersion was prepared by a coprecipitation method followed by the surface treatment with lauric acid and sodium dodecyl sulfate (SDS). On the Second step, the one-to-one copy of monomer droplets to latex particles could be synthesized via polymerization of an equilibrium stabilized miniemulsion prepared from a less stringent preparation process. The size distribution of obtained latex particles was relatively narrow. On the third step, by mixing the Fe3O4 dispersion with latex particles, the magnetic composite latex particles could be fabricated from heterocoagulation. Moreover, an all-atom molecular dynamics simulations were employed to explore the influences of sizes and surface polarity of polymer and inorganic particles on the morphology of composite latex particles. The simulation results were in agreement with our experimental results. In the second part, P(AA-SA) latex particles were synthesized via W/O miniemulsion polymerization. In order to minimize the monomer dissolving in continuous phase, cyclohexane, the polymerization was carried out in the presence of ammonium persulfate/sodium metabisulfite (APS/SMBS) redox initiators at 0-5oC. The influences of costabilizer on the stability of miniemulsion, its morphology and nucleation mechanism were studied. The pKa and pH regulation capacity of P(AA-SA) latex particles synthesized in this work were investigated in depth. Furthermore, the ZnO nanoparticles were fabricated by a hydrothermal synthesis method in ethanol followed by oleic acid surface modification for dispersing the nanoparticles in cyclohexane. Based on our previous experimental procedure of synthesizing P(AA-SA) latex particles, P(AA-SA)/ZnO composite particles could be fabricated by introducing modified ZnO nanoparticles into continuous phase. ZnO was not only amphoteric substance but also owned an excellent performance in photocatalyst and UV shielding. The applications of P(AA-SA)/ZnO composite particles were various besides pH regulation. The reaction mechanism, morphology, pH regulation capacity and UV/Vis absorbance properties of composite latex particles were examined. In the third part of this work, 1,1-diphenylethene (DPE) was employed to control the living free radical polymerization of MMA. When the reaction temperature was low (less than 95oC), the molecular weight of synthesized polymer remained almost a constant throughout the reaction time regardless of changing the amounts of DPE and initiator. A living polymerization kinetic model was established and compared with our experimental results. The kinetic rate constants involved in the DPE mechanism were estimated. The rate constant k2, corresponding to the reactivation reaction of the DPE- capped-dormant chains, was found to be very small, that accounted for the result of a constant molecular weight of polymer synthesized throughout the polymerization. In order to increase k2, the polymerization temperature was increased to 135oC, and the molecular weight of polymers increases with conversion, demonstrating the living nature of DPE mechanism. In addition, by using a preheating treatment, all the initiators dissociated into radicals at the very beginning of the polymerization. Then the synthesized polymers with narrow molecular weight distribution could be prepared successfully. From the trace of GPC diagram, a unimodal rather than bimodal molecular weight distribution was observed throughout the polymerization. Finally, PMMA-b-PBA block copolymers were synthesized by two polymerization methods (homogeneous polymerization and miniemulsion polymerization) using DPE-containing PMMA as a macroinitiator. The influences of solvent and polymerization methods on the polymerization rate, controlled living character, molecular weight (Mn) and molecular weight distribution (PDI) throughout the polymerization were studied and discussed.

This paper presents the evolutionary programming with an ensemble of memories to deal with optimization problems in dynamic environments. The proposed algorithm modifies a recent version of evolutionary programming by introducing a simulated-annealing-like dynamic strategy parameter as well as applying local search towards the most improving directions. Diversity of the population is enhanced by an ensemble of external archives that serve as short-term and long-term memories. The archive members also act as the basic solutions when environmental changes occur. The algorithm is tested on a set of 6 multimodal problems with a total 49 change instances provided by CEC 2009 Competition on Evolutionary Computation in Dynamic and Uncertain Environments and the results are presented.

Many problems in the real-world have more than one objective, with at least two objectives in conflict with one another. In addition, at least one objective changes over time. These kinds of problems are called dynamic multi-objective optimisation problems (DMOOPs). Studies have shown that both the quantum particle swarm optimisation (QPSO) and charged particle swarm optimisation (CPSO) algorithms perform well in dynamic environments, since they maintain swarm diversity. Therefore, this paper investigates the effect of using either QPSOs or CPSOs in the sub-swarms of the dynamic vector-evaluated particle swarm optimisation (DVEPSO) algorithm. These DVEPSO variations are then compared against the default DVEPSO algorithm that uses gbest PSOs and DVEPSO using heterogeneous PSOs that contain both charged and quantum particles. Furthermore, all of the aforementioned DVEPSO configurations are compared against the dynamic multi-objective optimisation (DMOPSO) algorithm that was the winning algorithm of a comprehensive comparative study of dynamic multi-objective optimisation algorithms. The results indicate that charged and quantum particles improve the performance of DVEPSO, especially for DMOOPs with a deceptive POF and DMOOPs with a non-linear POS.

There has been a growing interest in studying evolutionary algorithms in dynamic environments in recent years due to its importance in real applications. However, different dynamic test problems have been used to test and compare the performance of algorithms. This paper proposes a generalized dynamic benchmark generator (GDBG) that can be instantiated into the binary space, real space and combinatorial space. This generator can present a set of different properties to test algorithms by tuning some control parameters. Some experiments are carried out on the real space to study the performance of the generator.

In this paper the optimized parameters of Lag-Lead and Proportional Integral Derivative (PID) controller for two-actuator based electromagnetic levitation system (EMLS) are determined using Evolutionary computation algorithms such as Particle Swarm Optimization (PSO) and a novel Gravitational Search algorithm (GSA). A comparative position response of EMLS (Electro Magnetic Levitation System) with classical, GSA and PSO based nominal controllers while operating at nominal and off-nominal (low and high) air-gaps have been studied. It is seen that GSA based optimized nominal PID controller shows excellent performance (both in transient and steady-state) over a large operating air-gap. The experimental results with the classical controller are also presented.

<p>Classification problems often have a large number of features, but not all of them are useful for classification. Irrelevant and redundant features may even reduce the classification accuracy. Feature selection is a process of selecting a subset of relevant features, which can decrease the dimensionality, shorten the running time, and/or improve the classification accuracy. There are two types of feature selection approaches, i.e. wrapper and filter approaches. Their main difference is that wrappers use a classification algorithm to evaluate the goodness of the features during the feature selection process while filters are independent of any classification algorithm. Feature selection is a difficult task because of feature interactions and the large search space. Existing feature selection methods suffer from different problems, such as stagnation in local optima and high computational cost. Evolutionary computation (EC) techniques are well-known global search algorithms. Particle swarm optimisation (PSO) is an EC technique that is computationally less expensive and can converge faster than other methods. PSO has been successfully applied to many areas, but its potential for feature selection has not been fully investigated. The overall goal of this thesis is to investigate and improve the capability of PSO for feature selection to select a smaller number of features and achieve similar or better classification performance than using all features. This thesis investigates the use of PSO for both wrapper and filter, and for both single objective and multi-objective feature selection, and also investigates the differences between wrappers and filters. This thesis proposes a new PSO based wrapper, single objective feature selection approach by developing new initialisation and updating mechanisms. The results show that by considering the number of features in the initialisation and updating procedures, the new algorithm can improve the classification performance, reduce the number of features and decrease computational time. This thesis develops the first PSO based wrapper multi-objective feature selection approach, which aims to maximise the classification accuracy and simultaneously minimise the number of features. The results show that the proposed multi-objective algorithm can obtain more and better feature subsets than single objective algorithms, and outperform other well-known EC based multi-objective feature selection algorithms. This thesis develops a filter, single objective feature selection approach based on PSO and information theory. Two measures are proposed to evaluate the relevance of the selected features based on each pair of features and a group of features, respectively. The results show that PSO and information based algorithms can successfully address feature selection tasks. The group based method achieves higher classification accuracies, but the pair based method is faster and selects smaller feature subsets. This thesis proposes the first PSO based multi-objective filter feature selection approach using information based measures. This work is also the first work using other two well-known multi-objective EC algorithms in filter feature selection, which are also used to compare the performance of the PSO based approach. The results show that the PSO based multiobjective filter approach can successfully address feature selection problems, outperform single objective filter algorithms and achieve better classification performance than other multi-objective algorithms. This thesis investigates the difference between wrapper and filter approaches in terms of the classification performance and computational time, and also examines the generality of wrappers. The results show that wrappers generally achieve better or similar classification performance than filters, but do not always need longer computational time than filters. The results also show that wrappers built with simple classification algorithms can be general to other classification algorithms.</p>

As cloud computing becomes increasingly prevalent, the detection and prevention of phishing URL attacks are essential, particularly in the Internet of Vehicles (IoV) environment, to maintain service reliability. In such a scenario, an attacker could send misleading phishing links, potentially compromising the system's functionality or, at worst, leading to a complete shutdown. To address these emerging threats, this study introduces a novel Dynamic Arithmetic Optimization Algorithm with Deep Learning-Driven Phishing URL Classification (DAOA-DLPC) model for cloud-enabled IoV infrastructure. The candidate's research utilizes character-level embeddings instead of word embeddings, as the former can capture intricate URL patterns more effectively. These embeddings are integrated with a deep learning model, the Multi-Head Attention and Bidirectional Gated Recurrent Units (MHA-BiGRU). To improve precision, hyperparameter tuning has been done using DAOA. The proposed method offers a feasible solution for identifying the phishing URLs, and the method achieves computational efficiency through the attention mechanism and dynamic hyperparameter optimization. The need for this work comes from the observation that the traditional machine learning approaches are not effective in dynamic environments like phishing threat landscapes in a dynamic environment such as the one of phishing threats. The presented DLPC approach is capable of learning new forms of phishing attacks in real time and reduce false positives. The experimental results show that the proposed DAOA-DLPC model outperforms the other models with an accuracy of 98.85%, recall of 98.49%, and F1-score of 98.38% and can effectively detect safe and phishing URLs in dynamic environments. These results imply that the proposed model is useful in distinguishing between safe and unsafe URLs than the conventional models.

In this paper we investigate a Self-Adaptive Differential Evolution algorithm (jDE) where F and CR control parameters are self-adapted and a multi-population method with aging mechanism is used. The performance of the jDE algorithm is evaluated on the set of benchmark functions provided for the CEC 2009 special session on evolutionary computation in dynamic and uncertain environments.

This chapter discusses the workings of PSO in two research fields with special importance in real-world applications, namely noisy and dynamic environments. Noise simulation schemes are presented and experimental results on benchmark problems are reported. In addition, we present the application of PSO on a simulated real world problem, namely the particle identification by light scattering. Moreover, a hybrid scheme that incorporates PSO in particle filtering methods to estimate system states online is analyzed, and representative experimental results are reported. Finally, the combination of noisy and continuously changing environments is shortly discussed, providing illustrative graphical representations of performance for different PSO variants. The text focuses on providing the basic concepts and problem formulations, and suggesting experimental settings reported in literature, rather than on the bibliographical presentation of the (prohibitively extensive) literature.

Poly(acrylic acid‐co‐sodium acrylate)/zinc oxide, P(AA‐SA)/ZnO, composite latex particles were synthesized by inverse miniemulsion polymerization. The ZnO nanoparticles were prepared by hydrothermal synthesis and undergone oleic acid (OA) surface treatment. The X‐ray diffraction pattern and FT‐IR spectra characterized the crystal structure and functional groups of OA‐ZnO nanoparticles. An appropriate formulation in preparing P(AA‐SA) latex particles, ensuring the dominant in situ particle nucleation and growth, was developed in our experiment first. Sodium hydroxide was chosen as a costabilizer, because of its ability to increase the deprotonation of acylic acid and enhance the hydrophilicity of monomer, acrylic acid besides providing osmotic pressure. The growth mechanism of P(AA‐SA)/ZnO composite particles was proposed. The OA‐ZnO nanoparticles were adsorbed on or around the surface of P(AA‐SA) latex particles by hydrophobic interaction, thus enhanced the interfacial tension over latex particles. The P(AA‐SA)/ZnO composite latex particles owned better thermal stability than pure latex particles. The pH regulation capacity was excellent for both ZnO and P(AA‐SA) particles. Combining P(AA‐SA) and ZnO nanoparticles into composite particles, the performance in pH regulation and UV shielding was discussed from our experimental results. © 2008 Wiley Periodicals, Inc. J Polym Sci Part A: Polym Chem 46: 8081–8090, 2008