A dissertation submitted to the Faculty of Engineering and the Built Environment, University of Witwatersrand, Johannesburg, in fulfilment of the requirements for the degree of Master of Science in Engineering, Johannesburg, June 2019 / Data from wearable proximity sensors can be used to measure and describe social contact patterns between individuals in a household. Previous work describing contact patterns, has been qualitative and relies on visual, subjective observations. Data of this kind has been collected for a short period of measurement ranging from 2-3 days. An automated, quantitative analysis of contact patterns could enable an accurate and new representation of social contact patterns. Data was collected from ten households, for 21 days in a pilot study implemented in South Africa. 20 datasets were analysed, representing contact events of 20 individuals. Principal Component Analysis was implemented to determine the similarity of contact events across the days of the experiment and to estimate the minimum number of days required to be sampled, to validly represent an individual’s contact activity. The results show that there is a great variation in contact activity across the days of the experiment, as represented by the number of clusters of similar days. The minimum number of days required was determined by the number of days that had a significant contribution to the first three principal components and this varied across individuals from 5 – 11 days. Further analysis on a larger cohort has a potential to provide better social contact parameters for complex social behavioural models and may assist in understanding transmission dynamics of respiratory pathogens, needed in public health research. / PH2020

Yes

No

This thesis studies a class of optimisation algorithms, namely Memetic Computing Structures, and proposes a novel set of promising algorithms that move the first step towards an implementation for the automatic generation of optimisation algorithms for continuous domains. This thesis after a thorough review of local search algorithms and popular meta-heuristics, focuses on Memetic Computing in terms of algorithm structures and design philosophy. In particular, most of the design carried out during my doctoral studies is inspired by the lex parsimoniae, aka Ockham’s Razor. It has been shown how simple algorithms, when well implemented can outperform complex implementations. In order to achieve this aim, the design is always carried out by attempting to identify the role of each algorithmic component/operator. In this thesis, on the basis of this logic, a set of variants of a recently proposed algorithms are presented. Subsequently a novel memetic structure, namely Parallel Memetic Structure is proposed and tested against modern algorithms representing the state of the art in optimisation. Furthermore, an initial prototype of an automatic design platform is also included. This prototype performs an analysis on separability of the optimisation problem and, on the basis of the analysis results, designs some parts of the parallel structure. Promising results are included. Finally, an investigation of the correlation among the variables and problem dimensionality has been performed. An extremely interesting finding of this thesis work is that the degree of correlation among the variables decreases when the dimensionality increases. As a direct consequence of this fact, large scale problems are to some extent easier to handle than problems in low dimensionality since, due to the lack of correlation among the variables, they can effectively be tackled by an algorithm that performs moves along the axes.

Η μελέτη των βιολογικών συστημάτων στα διαφορετικά επίπεδα οργάνωσης του κυττάρου είναι ένας τομέας που αναδύεται ταχύτατα στην περιοχή της υπολογιστικής βιολογίας. Η πλειοψηφία των ερευνών σε αυτό τον τομέα έχει επικεντρωθεί στον διαχωρισμό των γονιδίων σε βιολογικά μονοπάτια ή διεργασίες. Το επόμενο βήμα στην κατανόηση του κυττάρου στο συστημικό του επίπεδο είναι ο καθορισμός του τρόπου με τον οποίο οι συγκεκριμένες κυτταρικές διεργασίες λειτουργούν μαζί για να επιτελέσουν τις κυτταρικές λειτουργίες. Βασικός σκοπός της παρούσας διπλωματικής εργασίας είναι η πρόβλεψη αλληλεπιδράσεων διαφόρων ειδών οι οποίες λαμβάνουν μέρος στα διαφορετικά επίπεδα του κυττάρου καθώς και η διερεύνηση του τρόπου με τον οποίο αυτές οι αλληλεπιδράσεις συνεργάζονται μεταξύ τους έτσι ώστε να επιτελέσουν τις κυτταρικές λειτουργίες. Στο χαμηλότερο επίπεδο του κυττάρου υπάρχουν οι φυσικές αλληλεπιδράσεις οι οποίες ισοδυναμούν με σύνδεση των πρωτεϊνών (ή μιας πρωτεΐνης και ενός DNA μορίου) στον 3-διάστατο χώρο. Η σύνδεση αυτή μπορεί να έχει διάφορα αποτελέσματα, όπως η μεταφορά ενός βιοσήματος ή η δημιουργία ενός νέου βιομορίου. Σε ένα ανώτερο επίπεδο από τις φυσικές αλληλεπιδράσεις, πραγματοποιούνται οι λειτουργικές αλληλεπιδράσεις οι οποίες μπορούν σε γενικές γραμμές να κατηγοριοποιηθούν σε σειριακές λειτουργικές αλληλεπιδράσεις (δίκτυα ρυθμιστικών αλληλεπιδράσεων), παράλληλες λειτουργικές αλληλεπιδράσεις όπως για παράδειγμα η συνθετική θνησιμότητα (γενετικές αλληλεπιδράσεις) και συνεργατικές λειτουργικές αλληλεπιδράσεις, όπως για παράδειγμα τα πρωτεϊνικά σύμπλοκα. Οι βιολογικές διεργασίες οι οποίες δραστηριοποιούνται στο ανώτατο επίπεδο του κυττάρου είναι στην πραγματικότητα ομάδες πρωτεϊνών και γονιδίων τα οποία λειτουργούν συνεργατικά. Οι αλληλεπιδράσεις μεταξύ των βιολογικών διεργασιών είναι οι υψηλότερου κυτταρικού επιπέδου αλληλεπιδράσεις τις οποίες θα μπορούσαμε να ανιχνεύσουμε. Η ανίχνευση των παραπάνω διαφορετικών ειδών αλληλεπιδράσεων καθώς και η εννοιολογική σύνδεσή τους αποτελεί το αντικείμενο μελέτης της παρούσας διπλωματικής εργασίας. Η αναγκαία πληροφορία για να οδηγηθούμε στην πρόβλεψη αλληλεπιδράσεων του ανώτερου επιπέδου του κυττάρου είναι οι χαμηλού επιπέδου (physical) πρωτεϊνικές αλληλεπιδράσεις. Πολλές υπολογιστικές μέθοδοι έχουν εφαρμοστεί μέχρι στιγμής στο πρόβλημα της πρόβλεψης πρωτεϊνικών αλληλεπιδράσεων, οι οποίες όμως αποτυγχάνουν στην ταυτόχρονη επίτευξη καλής απόδοσης και ερμηνευσιμότητας. Στα πλαίσια της διπλωματικής εργασίας αναλύεται το πρόβλημα της πρόβλεψης πρωτεϊνικών αλληλεπιδράσεων. Περιγράφονται οι πιο πρόσφατες πειραματικές και υπολογιστικές μέθοδοι για την ανίχνευση τους. Αναλύονται οι διαφορές τους, τα πλεονεκτήματα και τα μειονεκτήματά τους και επιπλέον γίνεται μία προσπάθεια καταγραφής των στοιχείων που τις περιορίζουν και προτείνονται τρόποι για την μελλοντική εξέλιξη και βελτίωσή τους. Στην συνέχεια μελετάται ο τρόπος με τον οποίο η τοπολογία των δικτύων πρωτεϊνικών αλληλεπιδράσεων επηρεάζει τις λειτουργικές αλληλεπιδράσεις που εμφανίζονται στο εσωτερικό του κυττάρου, όπως για παράδειγμα τις ρυθμιστικές (regulatory) και τις επιστατικές (genetic) αλληλεπιδράσεις. Δημιουργείται ένα σταθμισμένο δίκτυο το οποίο περιέχει πληροφορία για τις αλληλεπιδράσεις μεταξύ των πρωτεϊνών στο φυσικό επίπεδο (physical interactions). Η εκμετάλλευση της τοπολογίας του δικτύου φυσικών αλληλεπιδράσεων γίνεται μέσω τεχνικών διάχυσης πυρήνων (kernel diffusion). Τροποποιώντας τον βαθμό της διάχυσης (degree of diffusion), δημιουργούνται τα προφιλ διάχυσης (diffusion profiles). Στην συνέχεια, αυτά τα προφίλ χρησιμοποιούνται προκειμένου να χαρακτηρίσουν τις τοπολογίες που συνδέουν τις πρωτεΐνες πάνω στο δίκτυο φυσικών αλληλεπιδράσεων. Επίσης τα προφίλ διάχυσης, αποδεικνύονται εξαιρετικά χρήσιμα εργαλεία στην βελτίωση της απόδοσης των αλγορίθμων πρόβλεψης λειτουργικών αλληλεπιδράσεων. Στην συνέχεια οι πρωτεϊνικές αλληλεπιδράσεις χρησιμοποιούνται εκ νέου προκειμένου να προβλεφθούν εξαρτήσεις σε ένα επίπεδο υψηλότερα των λειτουργικών αλληλεπιδράσεων και συγκεκριμένα μεταξύ βιολογικών διεργασιών όπως αυτές περιγράφονται στην βάση δεδομένων Gene Ontology. Η κλασσική προσέγγιση στην μελέτη πολύπλοκων βιολογικών δικτύων βασίζεται στην ταυτοποίηση αλληλεπιδράσεων μεταξύ εσωτερικών συστατικών μεταβολικών ή σηματιδικών μονοπατιών. Επιπλέον, γνωρίζουμε σήμερα πολύ λίγα πράγματα για τις αλληλεπιδράσεις μεταξύ βιολογικών συστημάτων ανώτερης τάξης, όπως είναι τα βιολογικά μονοπάτια και οι βιολογικές διεργασίες. Στα πλαίσια της διπλωματικής εργασίας προτείνεται μια μεθοδολογία για την εύρεση αλληλεπιδράσεων μεταξύ βιολογικών διεργασιών αναλύοντας σταθμισμένες και μη σταθμισμένες πρωτεϊνικές αλληλεπιδράσεις. Βασική απόρροια της διπλωματικής εργασίας είναι οι αλληλεπιδράσεις μεταξύ βιολογικών διεργασιών που προέκυψαν και μέσω των οποίων δημιουργείται ένα νεο είδος δικτύου, το δίκτυο αλληλεπιδράσεων μεταξύ βιολογικών διεργασιών. Διάφορες βάσεις δεδομένων έχουν σχεδιαστεί για την αποθήκευση πληροφορίας σχετικής με τις πειραματικά και υπολογιστικά ταυτοποιημένες ανθρώπινες πρωτεϊνικές αλληλεπιδράσεις. Ωστόσο, αυτές οι βάσεις δεδομένων περιέχουν πολλές λανθασμένα θετικές αλληλεπιδράσεις, έχουν χαμηλή κάλυψη και μόνο λίγες από αυτές ενσωματώνουν πληροφορία από διάφορες πηγές. Για την αποφυγή των παραπάνω προβλημάτων, έχει σχεδιαστεί η βάση δεδομένων ΗΙΝΤ-ΚΒ (http://150.140.142.24:84) η οποία είναι μία βάση γνώσης που ενσωματώνει δεδομένα από διάφορες πηγές, παρέχει ένα φιλικό περιβάλλον προς τον χρήστη για την ανάκτησή τους, υπολογίζει ένα σύνολο χαρακτηριστικών και ένα σκορ εμπιστοσύνης για κάθε πιθανή πρωτεϊνική αλληλεπίδραση. Το σκορ εμπιστοσύνης είναι βασικό για το φιλτράρισμα των λανθασμένα θετικών αλληλεπιδράσεων οι οποίες είναι παρούσες σε διάφορες υπάρχουσες βάσεις δεδομένων. Για το σκοπό αυτό δημιουργήθηκε μία νέα υβριδική μεθοδολογία μηχανικής μάθησης, η οποία ονομάζεται Μαθηματική Μοντελοποίηση Εξελικτικού Κάλμαν (ΜΜΕΚ) για την επίτευξη μιας ακριβούς και ερμηνεύσιμης διαδικασίας ανάθεσης βαρών στις πρωτεϊνικές αλληλεπιδράσεις. Τα πειραματικά αποτελέσματα καταδεικνύουν ότι η συγκεκριμένη μέθοδος υπερτερεί σε σχέση με τις πιο γνωστές μεθόδους πρόβλεψης πρωτεϊνικών αλληλεπιδράσεων. Τα αποτελέσματα της διπλωματικής εργασίας φιλοδοξείται να συμβάλλουν στην πρόβλεψη νέων πιθανών αλληλεπιδράσεων του χαμηλού και του υψηλού κυτταρικού επιπέδου του ανθρώπινου οργανισμού και του οργανισμού του Ζακχαρομήκυτα (S. cerevisiae). Επιπλέον, μπορούν να χρησιμοποιηθούν για την κατανόηση των ανώτερων επιπέδων οργάνωσης του κυττάρου σαν ένα ενιαίο σύστημα. Τέλος, μία ακόμη σημαντική απόρροια που προκύπτει από την ανάλυση που παρέχεται από την διπλωματική εργασία είναι η ανάγκη επανεξέτασης της state-of-the-art προσεγγίσης της βάσης δεδομένων Gene Ontology για την οργάνωση της βιολογικής γνώσης. / The study of biological systems at different levels of organization is a rapidly emerging area of computational biology. The majority of research in this field has focused on partitioning genes into biological pathways or processes. The next hurdle in moving towards the goal of understanding the cell at a systems level is to determine how these partitioned cellular processes work together to achieve the cell’s objectives. The main goal of the thesis is the prediction of various kinds of interactions that take place in the different levels of the cell and the examination of the way that these interactions cooperate in order to fullfill the cell functions. At the lower level of the cell the physical interactions exist which entail the full range of chemical bonds between proteins DNA molecules. In addition to these physical descriptions, also functional descriptions of the cellular system can be determined. These can be broadly categorized into 1) serial function interactions, such as the regulatory network interactions, 2) parallel function interactions, such as epistatic interactions (e.g. synthetic lethality) and 3) collaborative function interactions, such as protein complexes. The biological processes which exist at the highest level of the cell are groups of proteins and genes that function collaboratively. The interactions between biological processes are the highest cellular level interactions that we can detect. The detection of the aforementioned different kinds of cellular interactions as well as their conceptual linkage is the subject that the current thesis focus on. The necessary information that leads to the prediction of interactions at the higher level of the cell is the lower level physical protein interactions. Many computational methods have been implemented so far to the problem of predicting protein interactions, without achieving at the same time high performance and interpretability. At the framework of the current thesis the problem of PPI prediction is analyzed. The most contemporary experimental and computational methods for detecting PPIs are described. We will analyze their differences, advantages, disadvantages and restrictions and moreover ways for their future improvement and development are discussed. Next, we focus on the way that the topology of the physical interaction network effects on the functional interactions that take place inside the cell, such as the regulatory and the genetic interactions. A physical protein interaction network is been constructed. The topology of that network is been exploited by using kernel diffusion techniques. By varying the diffusion degree, the diffusion profiles are been created. Next, the diffusion profiles are used to characterize the topologies that connect the proteins on the physical interaction network. Moreover, the diffusion profiles are proved to be excellent tools in the improvement of the performance of the algorithms that focus on the prediction of functional interactions. Next, protein interactions are been utilized again to predict interactions at a level above the functional interactions and that is the interactions of the biological processes as they are described in the Gene Ontology database. The classical approach for studying the complex biological networks is based on the identification of interactions between the internal components metabolic or signaling pathways. Moreover, very little is known nowadays about the interactions between higher order biological systems, such as the biological processes and pathways. In the framework of the current thesis, a new methodology for the detection of interactions between biological processes is been proposed. The methodology analyzes weighted or not protein interactions. The major result of the thesis is the network constructed by using the predicted interactions between biological processes, the so called biological processes interaction network. Various databases have been developed containing information about experimentally and computationally detected human PPIs as well as their corresponding annotation data. However, these databases contain many false positive interactions, are partial and only a few of them incorporate data from various sources. To overcome these limitations, we have developed HINT-KB (http://150.140.142.24:84), a knowledge base that integrates data from various sources, provides a user-friendly interface for their retrieval, calculates a set of features of interest and computes a confidence score for every candidate protein interaction. This confidence score is essential for filtering the false positive interactions which are present in existing databases, predicting new protein interactions and measuring the frequency of each true protein interaction. For this reason, a novel machine learning hybrid methodology, called (Evolutionary Kalman Mathematical Modelling - EvoKalMaModel), was used to achieve an accurate and interpretable scoring methodology. The experimental results indicated that the proposed scoring scheme outperforms existing computational methods for the prediction of PPIs. The results of the current thesis are expected to contribute in the prediction of new potential interaction of the lower and the higher cell level for the two organisms of Human and S. Cerevisiae. Moreover, they can used for understanding the higher organizational cell levels as a compact system. Finally, the results are expected to enhance the possibility of reconstructing the state-of-the-art approaches for organizing the biological knowledge.

Summary Multi-Agent systems typically utilise simple, predictable agents. The usage of such agents in large systems allows for complexity to be achieved through the interaction of these agents. It is feasible, however, to utilise intelligent agents in smaller systems, allowing for more agent complexity and hence a higher degree of realism in the multi-agent model. By utilising the TD( ) Algorithm to train feedforward neural networks, intelligent agents were successfully trained within the reinforcement learning paradigm. A methodology for stabilising this typically unstable neural network training was found through first looking at the relatively simple problem of Tic-Tac-Toe. Once a stable training methodology was arrived at, the more complex task of tackling a multi-player, multi-stage card-game was tackled. The results illustrated that a variety of scenarios can be realistically investigated through the multi-agent model, allowing for solving of situations and better understanding of the game itself. Yet more startling, owing to the agent’s design, the agents learned on their own to bluff, giving much greater insight into the nature of bluffing in such games that lend themselves to the act.

This thesis concerns the design, construction, control, and testing of a novel self-contained soft robotic vehicle; the JenniFish is a free-swimming jellyfish-like soft robot that could be adapted for a variety of uses, including: low frequency, low power sensing applications; swarm robotics; a STEM classroom learning resource; etc. The final vehicle design contains eight PneuNet-type actuators radially situated around a 3D printed electronics canister. These propel the vehicle when inflated with water from its surroundings by impeller pumps; since the actuators are connected in two neighboring groups of four, the JenniFish has bi-directional movement capabilities. Imbedded resistive flex sensors provide actuator position to the vehicle’s PD controller. Other onboard sensors include an IMU and an external temperature sensor. Quantitative constrained load cell tests, both in-line and bending, as well as qualitative free-swimming video tests were conducted to find baseline vehicle performance capabilities. Collected metrics compare well with existing robotic jellyfish. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2016. / FAU Electronic Theses and Dissertations Collection

Symmetry has been a very useful tool to researchers in various scientific fields. At its most basic, symmetry refers to the invariance of an object to some transformation, or set of transformations. Usually one searches for, and uses information concerning an existing symmetry within given data, structure or concept to somehow improve algorithm performance or compress the search space. This thesis examines the effects of imposing or inducing symmetry on a search space. That is, the question being asked is whether only existing symmetries can be useful, or whether changing reference to an intuition-based definition of symmetry over the evaluation function can also be of use. Within the context of optimization, symmetry induction as defined in this thesis will have the effect of equating the evaluation of a set of given objects. Group theory is employed to explore possible symmetrical structures inherent in a search space. Additionally, conditions when the search space can have a symmetry induced on it are examined. The idea of a neighborhood structure then leads to the idea of opposition-based computing which aims to induce a symmetry of the evaluation function. In this context, the search space can be seen as having a symmetry imposed on it. To be useful, it is shown that an opposite map must be defined such that it equates elements of the search space which have a relatively large difference in their respective evaluations. Using this idea a general framework for employing opposition-based ideas is proposed. To show the efficacy of these ideas, the framework is applied to popular computational intelligence algorithms within the areas of Monte Carlo optimization, estimation of distribution and neural network learning. The first example application focuses on simulated annealing, a popular Monte Carlo optimization algorithm. At a given iteration, symmetry is induced on the system by considering opposite neighbors. Using this technique, a temporary symmetry over the neighborhood region is induced. This simple algorithm is benchmarked using common real optimization problems and compared against traditional simulated annealing as well as a randomized version. The results highlight improvements in accuracy, reliability and convergence rate. An application to image thresholding further confirms the results. Another example application, population-based incremental learning, is rooted in estimation of distribution algorithms. A major problem with these techniques is a rapid loss of diversity within the samples after a relatively low number of iterations. The opposite sample is introduced as a remedy to this problem. After proving an increased diversity, a new probability update procedure is designed. This opposition-based version of the algorithm is benchmarked using common binary optimization problems which have characteristics of deceptivity and attractive basins characteristic of difficult real world problems. Experiments reveal improvements in diversity, accuracy, reliability and convergence rate over the traditional approach. Ten instances of the traveling salesman problem and six image thresholding problems are used to further highlight the improvements. Finally, gradient-based learning for feedforward neural networks is improved using opposition-based ideas. The opposite transfer function is presented as a simple adaptive neuron which easily allows for efficiently jumping in weight space. It is shown that each possible opposite network represents a unique input-output mapping, each having an associated effect on the numerical conditioning of the network. Experiments confirm the potential of opposite networks during pre- and early training stages. A heuristic for efficiently selecting one opposite network per epoch is presented. Benchmarking focuses on common classification problems and reveals improvements in accuracy, reliability, convergence rate and generalization ability over common backpropagation variants. To further show the potential, the heuristic is applied to resilient propagation where similar improvements are also found.

Symmetry has been a very useful tool to researchers in various scientific fields. At its most basic, symmetry refers to the invariance of an object to some transformation, or set of transformations. Usually one searches for, and uses information concerning an existing symmetry within given data, structure or concept to somehow improve algorithm performance or compress the search space. This thesis examines the effects of imposing or inducing symmetry on a search space. That is, the question being asked is whether only existing symmetries can be useful, or whether changing reference to an intuition-based definition of symmetry over the evaluation function can also be of use. Within the context of optimization, symmetry induction as defined in this thesis will have the effect of equating the evaluation of a set of given objects. Group theory is employed to explore possible symmetrical structures inherent in a search space. Additionally, conditions when the search space can have a symmetry induced on it are examined. The idea of a neighborhood structure then leads to the idea of opposition-based computing which aims to induce a symmetry of the evaluation function. In this context, the search space can be seen as having a symmetry imposed on it. To be useful, it is shown that an opposite map must be defined such that it equates elements of the search space which have a relatively large difference in their respective evaluations. Using this idea a general framework for employing opposition-based ideas is proposed. To show the efficacy of these ideas, the framework is applied to popular computational intelligence algorithms within the areas of Monte Carlo optimization, estimation of distribution and neural network learning. The first example application focuses on simulated annealing, a popular Monte Carlo optimization algorithm. At a given iteration, symmetry is induced on the system by considering opposite neighbors. Using this technique, a temporary symmetry over the neighborhood region is induced. This simple algorithm is benchmarked using common real optimization problems and compared against traditional simulated annealing as well as a randomized version. The results highlight improvements in accuracy, reliability and convergence rate. An application to image thresholding further confirms the results. Another example application, population-based incremental learning, is rooted in estimation of distribution algorithms. A major problem with these techniques is a rapid loss of diversity within the samples after a relatively low number of iterations. The opposite sample is introduced as a remedy to this problem. After proving an increased diversity, a new probability update procedure is designed. This opposition-based version of the algorithm is benchmarked using common binary optimization problems which have characteristics of deceptivity and attractive basins characteristic of difficult real world problems. Experiments reveal improvements in diversity, accuracy, reliability and convergence rate over the traditional approach. Ten instances of the traveling salesman problem and six image thresholding problems are used to further highlight the improvements. Finally, gradient-based learning for feedforward neural networks is improved using opposition-based ideas. The opposite transfer function is presented as a simple adaptive neuron which easily allows for efficiently jumping in weight space. It is shown that each possible opposite network represents a unique input-output mapping, each having an associated effect on the numerical conditioning of the network. Experiments confirm the potential of opposite networks during pre- and early training stages. A heuristic for efficiently selecting one opposite network per epoch is presented. Benchmarking focuses on common classification problems and reveals improvements in accuracy, reliability, convergence rate and generalization ability over common backpropagation variants. To further show the potential, the heuristic is applied to resilient propagation where similar improvements are also found.

Στην διατριβή αυτή εξετάζεται η αποδοτικότητα αλγορίθμων υπολογιστικής νοημοσύνης σε προβλήματα αριθμητικής βελτιστοποίησης, αναπτύσσονται τροποποιήσεις και βελτιώσεις των μεθόδων και εισάγεται ένα νέο σχήμα της μεθόδου Βελτιστοποίησης με Σμήνος Σωματιδίων, το οποίο ενοποιεί διαφορετικές εκδόσεις της συνδυάζοντας τα χαρακτηριστικά τους, μαζί με την θεωρητική ανάλυσή του. / The main goal of this thesis was the investigation of the performance of computational intelligence algorithms on numerical optimization problems, the development of modifications and improvements of the algorithms, as well as the development of a new scheme of the Particle Swarm Optimization algorithm that harnesses its main variants, along with its theoretical analysis.