The bat algorithm (BA) is a nature inspired algorithm which is mimicking the bio-sensing characte... more The bat algorithm (BA) is a nature inspired algorithm which is mimicking the bio-sensing characteristics of bats, known as echolocation. This paper suggests a Bat-based meta-heuristic for the inverse kinematics problem of a robotic arm. An intrinsically modified BA is proposed to find an inverse kinematics (IK) solution, respecting a minimum variation of the joints’ elongation from the initial configuration of the robot manipulator to the proposed new pause position. The proposed method is called IK-BA, it stands for a specific bat algorithm dedicated to robotic-arms’ inverse geometric solution, and where the elongation control mechanism is embedded in bat agents update equations. Performances analysis and comparatives to related state of art meta-heuristics solvers showed the effectiveness of the proposed IK bat solver for single point IK planning as well as for geometric path planning, which may have several industrial applications. IK-BA was also applied to a real robotic arm wit...
This book presents the latest research in hybrid intelligent systems. It includes 57 carefully se... more This book presents the latest research in hybrid intelligent systems. It includes 57 carefully selected papers from the 16th International Conference on Hybrid Intelligent Systems (HIS 2016) and the 8th World Congress on Nature and Biologically Inspired Computing (NaBIC 2016), held on November 21–23, 2016 in Marrakech, Morocco. HIS - NaBIC 2016 was jointly organized by the Machine Intelligence Research Labs (MIR Labs), USA; Hassan 1st University, Settat, Morocco and University of Sfax, Tunisia. Hybridization of intelligent systems is a promising research field in modern artificial/computational intelligence and is concerned with the development of the next generation of intelligent systems. The conference’s main aim is to inspire further exploration of the intriguing potential of hybrid intelligent systems and bio-inspired computing. As such, the book is a valuable resource for practicing engineers /scientists and researchers working in the field of computational intelligence and ar...
ESN is a simple and powerful network. It is simple thanks to its non-complex architecture as well... more ESN is a simple and powerful network. It is simple thanks to its non-complex architecture as well as its training method. It is powerful thanks to the good results given in the field of machine learning. Moreover, It has a special topology characterized by random parameters initialization especially those related to the reservoir and the weights. Although this random initialization is followed by some pre-treatments such as the scaling of the reservoir matrix by its spectral radius, it is still non sufficient to obtain satisfying results. As a remedy to this problem, PSO is used for the fine tuning of some of these parameters. In fact, the studied approach consists of doing a PSO pre-training of a subset or subsets from the reservoir, input and backward weight matrices. Hence, the network will not be tuned by fully aleatory variables.
In robotics, gaits generation is a first step of locomotion control strategies. A particle swarm ... more In robotics, gaits generation is a first step of locomotion control strategies. A particle swarm is used to generate the joints trajectories, such a proposal is an alternative to classical kinematics' modeling. Our proposal consists in a hybrid swarms placed in the skeleton-joints. The skeleton structure is directly inspired from a human-like locomotion system. A collaboration strategy is applied to extract efficient locomotion gaits ensuring the walker stability. The stability control uses a classical method based on the projection of the center of mass, COM, of the robot on the sustention polygon.
In this paper, a new modified particle swarm optimization, m-PSO, is proposed, in which the novel... more In this paper, a new modified particle swarm optimization, m-PSO, is proposed, in which the novelty consists of proposing a fitness-based particle swarm optimization algorithm, PSO, which adapts the particles’ behavior rather than the PSO parameters and where particles evolve differently considering their level of optimality. A multi-objective optimization, MO, approach is then built based on m-PSO. In the proposed method, particles with fitness better than the mean local best are only updated toward the global best, while others keep moving in a classical manner. The proposed m-PSO and its multi-objective version MO-m-PSO are then employed to solve the inverse kinematics of a 5-DOF robotic arm which is 3D-printed for educational use. In the MO-m-PSO approach of inverse kinematics, the arm IK problem is formulated as a multi-objective problem searching for an appropriate solution that takes into consideration the end-effector position and orientation with a Pareto front strategy. Th...
Fake account detection is a topical issue when many Online Social Networks (OSNs) encounter probl... more Fake account detection is a topical issue when many Online Social Networks (OSNs) encounter problems caused by a growing number of unethical online social activities. This study presents a new Quantum Beta-Distributed Multi-Objective Particle Swarm Optimization (QBD-MOPSO) system to detect fake accounts on Twitter. The proposed system aims to minimize two objective functions simultaneously: specifically features dimensionality and classification error rate. The QBD-MOPSO has two optimization profiles: the first uses a quantum behaved equation for improving the exploratory behaviour of PSO, while the second uses a beta function to enhance PSO’s exploitation. Six variants of the QBD-MOPSO approach are proposed to account for various data distribution types. The QBD-MOPSO system provides a feature selection technique based on the sigmoid function for position binary encoding. Each particle has a binary vector as a potential solution for feature subset selection, and a bit with the valu...
Multifactorial Optimization (MFO) and Evolutionary Transfer Optimization (ETO) are new optimizati... more Multifactorial Optimization (MFO) and Evolutionary Transfer Optimization (ETO) are new optimization challenging paradigms for which the multi-Objective Particle Swarm Optimization system (MOPSO) may be interesting despite limitations. MOPSO has been widely used in static/dynamic multi-objective optimization problems, while its potentials for multi-task optimization are not completely unveiled. This paper proposes a new Distributed Multifactorial Particle Swarm Optimization algorithm (DMFPSO) for multi-task optimization. This new system has a distributed architecture on a set of sub-swarms that are dynamically constructed based on the number of optimization tasks affected by each particle skill factor. DMFPSO is designed to deal with the issues of handling convergence and diversity concepts separately. DMFPSO uses Beta function to provide two optimized profiles with a dynamic switching behaviour. The first profile, Beta-1, is used for the exploration which aims to explore the search ...
Advances in Intelligent Systems and Computing, 2017
AS-PSO, ANT Supervised by PSO is hybrid hierarchical metaheuristic optimization method where PSO ... more AS-PSO, ANT Supervised by PSO is hybrid hierarchical metaheuristic optimization method where PSO optimizes ANT parameters to enhance its performances. In this paper, a focus is made on the impact of the ACO swarm size on AS-PSO performances for the Traveling Salesmen Problem (TSP) where AS-PSO is already known as a relevant solver. Investigations used the AS-PSO-2Opt with both inertia weight AS-PSO and Standard AS-PSO. To demonstrate the effects of ant numbers on AS-PSO-2Opt method, a selected set of test benches form TSPLIB, berlin52, st70 and eli101 was used. In this experimental study of the ant number is waved from five to the city number of each selected test benches. Therefore, experimental results showed that the best swarm size is equal to 20 and gives the best solution for all test benches.
In this paper a new technique is integrated to Multi-Objective Particle Swarm Optimization (MOPSO... more In this paper a new technique is integrated to Multi-Objective Particle Swarm Optimization (MOPSO) algorithm, named Pareto Neighborhood (PN) topology, to produce MOPSO-PN algorithm. This technique involves iteratively selecting a set of best solutions from the Pareto-Optimal-Fronts and trying to explore them in order to find better clustering results in the next iteration. MOPSO-PN was then used as a Multi?Objective Clustering Optimization (MOCO) Algorithm, it was tested on various datasets (real-life and artificial datasets). Two scenarios have been used to test the performances of MOPSO-PN for clustering: In the first scenario MOPSO-PN utilizes, as objective functions, two clusters validity index (Silhouette?Index and overall-cluster-deviation), three datasets for test, four algorithms for comparison and the average Minkowski Score as metric for evaluating the final clustering result; In the second scenario MOPSO-PN used, as objectives functions, three clusters validity index (I-i...
This paper focus on a complex problem of job shop scheduling where each jobs have a multiple poss... more This paper focus on a complex problem of job shop scheduling where each jobs have a multiple possible operations sequences. The resolution of this type of problem has not been treated in the literature. To solve this, a new algorithm based on Particle Swarm Optimization Global Velocity (PSOVG) was proposed. The objectif is to minimize the makespan. The simulation results show the efficiency of our proposed approach.
This paper presents a software application allowing to solve and compare the key metaheuristic ap... more This paper presents a software application allowing to solve and compare the key metaheuristic approaches for solving the Traveling Salesman Problem (TSP). The focus is based on Ant Colony Optimization (ACO) and its major hybridization schema. In this work, the hybridization ACO algorithm with local search approach and the impact of parameters while solving TSP are investigated. The paper presents results of an empirical study of the solution quality over computation time for Ant System (AS), Elitist Ant System (EAS), Best-Worst Ant System (BWAS), MAX–MIN Ant System (MMAS) and Ant Colony System (ACS), five well-known ACO algorithms. In addition, this paper describes ACO approach combined with local search approach as 2-Opt and 3-Opt algorithms to obtain the best solution compared to ACO without local search with fixed parameters setting. The simulation experiments results show that ACO hybridized with the local search algorithm is effective for solving TSP and for avoiding the prema...
2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2016
AS-PSO-2Opt is a new enhancement of the AS-PSO method. In the classical AS-PSO, the Ant heuristic... more AS-PSO-2Opt is a new enhancement of the AS-PSO method. In the classical AS-PSO, the Ant heuristic is used to optimize the tour length of a Traveling Salesman Problem, TSP, and PSO is applied to optimize three parameters of ACO, (α, β, ρ). The AS-PSO-2Opt consider a post processing resuming path redundancy, helping to improve local solutions and to decrease the probability of falling in local minimum. Applied to TSP, the method allowed retrieving a valuable path solution and a set of fitted parameters for ACO. The performance of the AS-PSO-2Opt is tested on nine different TSP test benches. Experimental results based on a statistical analysis showed that the new proposal performs better than key state of art methods using Genetic algorithm, Neural Network and ACO algorithm. The AS-PSO-2Opt performs better than close related methods such as PSO-ACO-3Opt [9] and ACO with ABC [19] for various test benches.
Dynamic multi-objective optimization problems (DMOPs) and Many-Objective Optimization Problems (M... more Dynamic multi-objective optimization problems (DMOPs) and Many-Objective Optimization Problems (MaOPs) are two classes of the optimization filed which have potential applications in engineering. Modified Multi-Objective Evolutionary Algorithms hybrid approaches seem to be suitable to effectively deal with such problems. However, the Crow Search Algorithm has not yet considered for both DMOP and MaOP. This paper proposes a Distributed Bi-behaviors Crow Search Algorithm (DB-CSA) with two different mechanisms, one corresponding to the search behavior and another to the exploitative behavior with a dynamic switch mechanism. The bi-behaviors CSA chasing profile is defined based on a large Gaussian-like Beta-1 function which ensures diversity enhancement, while the narrow Gaussian Beta-2 function is used to improve the solution tuning and convergence behavior. The DB-CSA approach is developed to solve several types of DMOPs and a set of MaOPs with 2, 3, 5, 7, 8, 10 and 15 objectives. The ...
The bat algorithm (BA) is a nature inspired algorithm which is mimicking the bio-sensing characte... more The bat algorithm (BA) is a nature inspired algorithm which is mimicking the bio-sensing characteristics of bats, known as echolocation. This paper suggests a Bat-based meta-heuristic for the inverse kinematics problem of a robotic arm. An intrinsically modified BA is proposed to find an inverse kinematics (IK) solution, respecting a minimum variation of the joints’ elongation from the initial configuration of the robot manipulator to the proposed new pause position. The proposed method is called IK-BA, it stands for a specific bat algorithm dedicated to robotic-arms’ inverse geometric solution, and where the elongation control mechanism is embedded in bat agents update equations. Performances analysis and comparatives to related state of art meta-heuristics solvers showed the effectiveness of the proposed IK bat solver for single point IK planning as well as for geometric path planning, which may have several industrial applications. IK-BA was also applied to a real robotic arm wit...
This book presents the latest research in hybrid intelligent systems. It includes 57 carefully se... more This book presents the latest research in hybrid intelligent systems. It includes 57 carefully selected papers from the 16th International Conference on Hybrid Intelligent Systems (HIS 2016) and the 8th World Congress on Nature and Biologically Inspired Computing (NaBIC 2016), held on November 21–23, 2016 in Marrakech, Morocco. HIS - NaBIC 2016 was jointly organized by the Machine Intelligence Research Labs (MIR Labs), USA; Hassan 1st University, Settat, Morocco and University of Sfax, Tunisia. Hybridization of intelligent systems is a promising research field in modern artificial/computational intelligence and is concerned with the development of the next generation of intelligent systems. The conference’s main aim is to inspire further exploration of the intriguing potential of hybrid intelligent systems and bio-inspired computing. As such, the book is a valuable resource for practicing engineers /scientists and researchers working in the field of computational intelligence and ar...
ESN is a simple and powerful network. It is simple thanks to its non-complex architecture as well... more ESN is a simple and powerful network. It is simple thanks to its non-complex architecture as well as its training method. It is powerful thanks to the good results given in the field of machine learning. Moreover, It has a special topology characterized by random parameters initialization especially those related to the reservoir and the weights. Although this random initialization is followed by some pre-treatments such as the scaling of the reservoir matrix by its spectral radius, it is still non sufficient to obtain satisfying results. As a remedy to this problem, PSO is used for the fine tuning of some of these parameters. In fact, the studied approach consists of doing a PSO pre-training of a subset or subsets from the reservoir, input and backward weight matrices. Hence, the network will not be tuned by fully aleatory variables.
In robotics, gaits generation is a first step of locomotion control strategies. A particle swarm ... more In robotics, gaits generation is a first step of locomotion control strategies. A particle swarm is used to generate the joints trajectories, such a proposal is an alternative to classical kinematics' modeling. Our proposal consists in a hybrid swarms placed in the skeleton-joints. The skeleton structure is directly inspired from a human-like locomotion system. A collaboration strategy is applied to extract efficient locomotion gaits ensuring the walker stability. The stability control uses a classical method based on the projection of the center of mass, COM, of the robot on the sustention polygon.
In this paper, a new modified particle swarm optimization, m-PSO, is proposed, in which the novel... more In this paper, a new modified particle swarm optimization, m-PSO, is proposed, in which the novelty consists of proposing a fitness-based particle swarm optimization algorithm, PSO, which adapts the particles’ behavior rather than the PSO parameters and where particles evolve differently considering their level of optimality. A multi-objective optimization, MO, approach is then built based on m-PSO. In the proposed method, particles with fitness better than the mean local best are only updated toward the global best, while others keep moving in a classical manner. The proposed m-PSO and its multi-objective version MO-m-PSO are then employed to solve the inverse kinematics of a 5-DOF robotic arm which is 3D-printed for educational use. In the MO-m-PSO approach of inverse kinematics, the arm IK problem is formulated as a multi-objective problem searching for an appropriate solution that takes into consideration the end-effector position and orientation with a Pareto front strategy. Th...
Fake account detection is a topical issue when many Online Social Networks (OSNs) encounter probl... more Fake account detection is a topical issue when many Online Social Networks (OSNs) encounter problems caused by a growing number of unethical online social activities. This study presents a new Quantum Beta-Distributed Multi-Objective Particle Swarm Optimization (QBD-MOPSO) system to detect fake accounts on Twitter. The proposed system aims to minimize two objective functions simultaneously: specifically features dimensionality and classification error rate. The QBD-MOPSO has two optimization profiles: the first uses a quantum behaved equation for improving the exploratory behaviour of PSO, while the second uses a beta function to enhance PSO’s exploitation. Six variants of the QBD-MOPSO approach are proposed to account for various data distribution types. The QBD-MOPSO system provides a feature selection technique based on the sigmoid function for position binary encoding. Each particle has a binary vector as a potential solution for feature subset selection, and a bit with the valu...
Multifactorial Optimization (MFO) and Evolutionary Transfer Optimization (ETO) are new optimizati... more Multifactorial Optimization (MFO) and Evolutionary Transfer Optimization (ETO) are new optimization challenging paradigms for which the multi-Objective Particle Swarm Optimization system (MOPSO) may be interesting despite limitations. MOPSO has been widely used in static/dynamic multi-objective optimization problems, while its potentials for multi-task optimization are not completely unveiled. This paper proposes a new Distributed Multifactorial Particle Swarm Optimization algorithm (DMFPSO) for multi-task optimization. This new system has a distributed architecture on a set of sub-swarms that are dynamically constructed based on the number of optimization tasks affected by each particle skill factor. DMFPSO is designed to deal with the issues of handling convergence and diversity concepts separately. DMFPSO uses Beta function to provide two optimized profiles with a dynamic switching behaviour. The first profile, Beta-1, is used for the exploration which aims to explore the search ...
Advances in Intelligent Systems and Computing, 2017
AS-PSO, ANT Supervised by PSO is hybrid hierarchical metaheuristic optimization method where PSO ... more AS-PSO, ANT Supervised by PSO is hybrid hierarchical metaheuristic optimization method where PSO optimizes ANT parameters to enhance its performances. In this paper, a focus is made on the impact of the ACO swarm size on AS-PSO performances for the Traveling Salesmen Problem (TSP) where AS-PSO is already known as a relevant solver. Investigations used the AS-PSO-2Opt with both inertia weight AS-PSO and Standard AS-PSO. To demonstrate the effects of ant numbers on AS-PSO-2Opt method, a selected set of test benches form TSPLIB, berlin52, st70 and eli101 was used. In this experimental study of the ant number is waved from five to the city number of each selected test benches. Therefore, experimental results showed that the best swarm size is equal to 20 and gives the best solution for all test benches.
In this paper a new technique is integrated to Multi-Objective Particle Swarm Optimization (MOPSO... more In this paper a new technique is integrated to Multi-Objective Particle Swarm Optimization (MOPSO) algorithm, named Pareto Neighborhood (PN) topology, to produce MOPSO-PN algorithm. This technique involves iteratively selecting a set of best solutions from the Pareto-Optimal-Fronts and trying to explore them in order to find better clustering results in the next iteration. MOPSO-PN was then used as a Multi?Objective Clustering Optimization (MOCO) Algorithm, it was tested on various datasets (real-life and artificial datasets). Two scenarios have been used to test the performances of MOPSO-PN for clustering: In the first scenario MOPSO-PN utilizes, as objective functions, two clusters validity index (Silhouette?Index and overall-cluster-deviation), three datasets for test, four algorithms for comparison and the average Minkowski Score as metric for evaluating the final clustering result; In the second scenario MOPSO-PN used, as objectives functions, three clusters validity index (I-i...
This paper focus on a complex problem of job shop scheduling where each jobs have a multiple poss... more This paper focus on a complex problem of job shop scheduling where each jobs have a multiple possible operations sequences. The resolution of this type of problem has not been treated in the literature. To solve this, a new algorithm based on Particle Swarm Optimization Global Velocity (PSOVG) was proposed. The objectif is to minimize the makespan. The simulation results show the efficiency of our proposed approach.
This paper presents a software application allowing to solve and compare the key metaheuristic ap... more This paper presents a software application allowing to solve and compare the key metaheuristic approaches for solving the Traveling Salesman Problem (TSP). The focus is based on Ant Colony Optimization (ACO) and its major hybridization schema. In this work, the hybridization ACO algorithm with local search approach and the impact of parameters while solving TSP are investigated. The paper presents results of an empirical study of the solution quality over computation time for Ant System (AS), Elitist Ant System (EAS), Best-Worst Ant System (BWAS), MAX–MIN Ant System (MMAS) and Ant Colony System (ACS), five well-known ACO algorithms. In addition, this paper describes ACO approach combined with local search approach as 2-Opt and 3-Opt algorithms to obtain the best solution compared to ACO without local search with fixed parameters setting. The simulation experiments results show that ACO hybridized with the local search algorithm is effective for solving TSP and for avoiding the prema...
2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2016
AS-PSO-2Opt is a new enhancement of the AS-PSO method. In the classical AS-PSO, the Ant heuristic... more AS-PSO-2Opt is a new enhancement of the AS-PSO method. In the classical AS-PSO, the Ant heuristic is used to optimize the tour length of a Traveling Salesman Problem, TSP, and PSO is applied to optimize three parameters of ACO, (α, β, ρ). The AS-PSO-2Opt consider a post processing resuming path redundancy, helping to improve local solutions and to decrease the probability of falling in local minimum. Applied to TSP, the method allowed retrieving a valuable path solution and a set of fitted parameters for ACO. The performance of the AS-PSO-2Opt is tested on nine different TSP test benches. Experimental results based on a statistical analysis showed that the new proposal performs better than key state of art methods using Genetic algorithm, Neural Network and ACO algorithm. The AS-PSO-2Opt performs better than close related methods such as PSO-ACO-3Opt [9] and ACO with ABC [19] for various test benches.
Dynamic multi-objective optimization problems (DMOPs) and Many-Objective Optimization Problems (M... more Dynamic multi-objective optimization problems (DMOPs) and Many-Objective Optimization Problems (MaOPs) are two classes of the optimization filed which have potential applications in engineering. Modified Multi-Objective Evolutionary Algorithms hybrid approaches seem to be suitable to effectively deal with such problems. However, the Crow Search Algorithm has not yet considered for both DMOP and MaOP. This paper proposes a Distributed Bi-behaviors Crow Search Algorithm (DB-CSA) with two different mechanisms, one corresponding to the search behavior and another to the exploitative behavior with a dynamic switch mechanism. The bi-behaviors CSA chasing profile is defined based on a large Gaussian-like Beta-1 function which ensures diversity enhancement, while the narrow Gaussian Beta-2 function is used to improve the solution tuning and convergence behavior. The DB-CSA approach is developed to solve several types of DMOPs and a set of MaOPs with 2, 3, 5, 7, 8, 10 and 15 objectives. The ...
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Papers by Nizar Rokbani