The Experts below are selected from a list of 95943 Experts worldwide ranked by ideXlab platform
E Garciagonzalo - One of the best experts on this subject based on the ideXlab platform.
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stochastic stability analysis of the linear continuous and discrete Pso models
IEEE Transactions on Evolutionary Computation, 2011Co-Authors: Juan Luis Fernandezmartinez, E GarciagonzaloAbstract:Particle swarm optimization (Pso) can be interpreted physically as a particular discretization of a stochastic damped mass-spring system. Knowledge of this analogy has been crucial to derive the Pso continuous model and to introduce different Pso family members including the generalized Pso (GPso) algorithm, which is the generalization of Pso for any time discretization step. In this paper, we present the stochastic analysis of the linear continuous and generalized Pso models for the case of a stochastic center of attraction. Analysis of the GPso second order trajectories is performed and clarifies the roles of the Pso parameters and that of the cost function through the algorithm execution: while the Pso parameters mainly control the eigenvalues of the dynamical systems involved, the mean trajectory of the center of attraction and its covariance functions with the trajectories and their derivatives (or the trajectories in the near past) act as forcing terms to update first and second order trajectories. The similarity between the oscillation center dynamics observed for different kinds of benchmark functions might explain the Pso success for a broad range of optimization problems. Finally, a comparison between real simulations and the linear continuous Pso and GPso models is shown. As expected, the GPso tends to the continuous Pso when time step approaches zero. Both models account fairly well for the dynamics (first and second order moments) observed in real runs. This analysis constitutes so far the most realistic attempt to better understand and approach the real Pso dynamics from a stochastic point of view.
Pooja Verma - One of the best experts on this subject based on the ideXlab platform.
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State-of-the-Art Reviews of Meta-Heuristic Algorithms with Their Novel Proposal for Unconstrained Optimization and Applications
Archives of Computational Methods in Engineering, 2021Co-Authors: Raghav Prasad Parouha, Pooja VermaAbstract:A widespread survey of numerous traditional meta-heuristic algorithms has been investigated category wise in this paper. Where, particle swarm optimization (Pso) and differential evolution (DE) is found to be an efficient and powerful optimization algorithm. Therefore, an extensive survey of recent-past Pso and DE variants with their hybrids has been inspected again. After this a novel Pso (called, nPso) and DE (namely, nDE) with their innovative hybrid (termed as, i h PsoDE) is proposed in this paper for unconstrained optimization problems. In nPso introducing a new linearly decreased inertia weight and gradually decreased and/or increased acceleration coefficient as well a different position update equation (by introducing a non-linear decreasing factor. And in nDE a new mutation strategy and crossover rate are introduced. In view of that, convergence characteristic of nPso and nDE provides different approximation to the solution space. Further, instead of naïve way proposed hybrid i h PsoDE integrating merits of nPso and nDE. In i h PsoDE after initialization and calculation identify best half member and discard rest of members from the population. In current population apply nPso to maintain exploration and exploitation. Then to enhance local search ability and improve convergence accuracy applies nDE. Hence, proposed i h PsoDE has higher probability of avoiding local optima and it is likely to find global optima more quickly due to relating superior capability of the anticipated nPso and nDE. Performance of the proposed hybrid i h PsoDE as well as its anticipated integrating component nPso and nDE are verified on 23 basic, 30 CEC 2014 and 30 CEC 2017 unconstrained benchmark functions plus 3 real world problems. The several numerical, statistical and graphical as well as comparative analyses over many state-of-the-art algorithms confirm superiority of the proposed algorithms. Finally, based on overall performance i h PsoDE is recommended for unconstrained optimization problems in this present study.
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An innovative hybrid algorithm for bound-unconstrained optimization problems and applications
Journal of Intelligent Manufacturing, 2021Co-Authors: Raghav Prasad Parouha, Pooja VermaAbstract:Particle swarm optimization (Pso) and differential evolution (DE) are two efficient meta-heuristic algorithms, achieving excellent performance in a wide variety of optimization problems. Unfortunately, when both algorithms are used to solve complex problems then they inevitably suffer from stagnation, premature convergence and unbalanced exploration–exploitation. Hybridization of Pso and DE may provide a platform to resolve these issues. Therefore, this paper proposes an innovative hybrid algorithm (i h PsoDE) which would be more effective than Pso and DE. It integrated with suggested novel Pso (nPso) and DE (nDE). Where in nPso a new, inertia weight and acceleration coefficient as well as position update equation are familiarized, to escape stagnation. And in nDE a new, mutation strategy and crossover rate is introduced, to avoid premature convergence. In order to balance between global and local search capability, after calculation of i h PsoDE population best half member has been identified and discard rest members. Further, in current population nPso is employed to maintain exploration and exploitation, then nDE is used to enhance convergence accuracy. The proposed i h PsoDE and its integrating component nPso and nDE have been tested over 23 basic, 30 IEEE CEC2014 and 30 IEEE CEC2017 unconstrained benchmark functions plus 3 real life optimization problems. The performance of proposed algorithms compared with traditional Pso and DE, their existed variants/hybrids as well as some of the other state-of-the-art algorithms. The results indicate the superiority of proposed algorithms. Finally, based on overall performance i h PsoDE is recommended for bound-unconstrained optimization problems in this present study.
Juan Luis Fernandezmartinez - One of the best experts on this subject based on the ideXlab platform.
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stochastic stability analysis of the linear continuous and discrete Pso models
IEEE Transactions on Evolutionary Computation, 2011Co-Authors: Juan Luis Fernandezmartinez, E GarciagonzaloAbstract:Particle swarm optimization (Pso) can be interpreted physically as a particular discretization of a stochastic damped mass-spring system. Knowledge of this analogy has been crucial to derive the Pso continuous model and to introduce different Pso family members including the generalized Pso (GPso) algorithm, which is the generalization of Pso for any time discretization step. In this paper, we present the stochastic analysis of the linear continuous and generalized Pso models for the case of a stochastic center of attraction. Analysis of the GPso second order trajectories is performed and clarifies the roles of the Pso parameters and that of the cost function through the algorithm execution: while the Pso parameters mainly control the eigenvalues of the dynamical systems involved, the mean trajectory of the center of attraction and its covariance functions with the trajectories and their derivatives (or the trajectories in the near past) act as forcing terms to update first and second order trajectories. The similarity between the oscillation center dynamics observed for different kinds of benchmark functions might explain the Pso success for a broad range of optimization problems. Finally, a comparison between real simulations and the linear continuous Pso and GPso models is shown. As expected, the GPso tends to the continuous Pso when time step approaches zero. Both models account fairly well for the dynamics (first and second order moments) observed in real runs. This analysis constitutes so far the most realistic attempt to better understand and approach the real Pso dynamics from a stochastic point of view.
Raghav Prasad Parouha - One of the best experts on this subject based on the ideXlab platform.
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State-of-the-Art Reviews of Meta-Heuristic Algorithms with Their Novel Proposal for Unconstrained Optimization and Applications
Archives of Computational Methods in Engineering, 2021Co-Authors: Raghav Prasad Parouha, Pooja VermaAbstract:A widespread survey of numerous traditional meta-heuristic algorithms has been investigated category wise in this paper. Where, particle swarm optimization (Pso) and differential evolution (DE) is found to be an efficient and powerful optimization algorithm. Therefore, an extensive survey of recent-past Pso and DE variants with their hybrids has been inspected again. After this a novel Pso (called, nPso) and DE (namely, nDE) with their innovative hybrid (termed as, i h PsoDE) is proposed in this paper for unconstrained optimization problems. In nPso introducing a new linearly decreased inertia weight and gradually decreased and/or increased acceleration coefficient as well a different position update equation (by introducing a non-linear decreasing factor. And in nDE a new mutation strategy and crossover rate are introduced. In view of that, convergence characteristic of nPso and nDE provides different approximation to the solution space. Further, instead of naïve way proposed hybrid i h PsoDE integrating merits of nPso and nDE. In i h PsoDE after initialization and calculation identify best half member and discard rest of members from the population. In current population apply nPso to maintain exploration and exploitation. Then to enhance local search ability and improve convergence accuracy applies nDE. Hence, proposed i h PsoDE has higher probability of avoiding local optima and it is likely to find global optima more quickly due to relating superior capability of the anticipated nPso and nDE. Performance of the proposed hybrid i h PsoDE as well as its anticipated integrating component nPso and nDE are verified on 23 basic, 30 CEC 2014 and 30 CEC 2017 unconstrained benchmark functions plus 3 real world problems. The several numerical, statistical and graphical as well as comparative analyses over many state-of-the-art algorithms confirm superiority of the proposed algorithms. Finally, based on overall performance i h PsoDE is recommended for unconstrained optimization problems in this present study.
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An innovative hybrid algorithm for bound-unconstrained optimization problems and applications
Journal of Intelligent Manufacturing, 2021Co-Authors: Raghav Prasad Parouha, Pooja VermaAbstract:Particle swarm optimization (Pso) and differential evolution (DE) are two efficient meta-heuristic algorithms, achieving excellent performance in a wide variety of optimization problems. Unfortunately, when both algorithms are used to solve complex problems then they inevitably suffer from stagnation, premature convergence and unbalanced exploration–exploitation. Hybridization of Pso and DE may provide a platform to resolve these issues. Therefore, this paper proposes an innovative hybrid algorithm (i h PsoDE) which would be more effective than Pso and DE. It integrated with suggested novel Pso (nPso) and DE (nDE). Where in nPso a new, inertia weight and acceleration coefficient as well as position update equation are familiarized, to escape stagnation. And in nDE a new, mutation strategy and crossover rate is introduced, to avoid premature convergence. In order to balance between global and local search capability, after calculation of i h PsoDE population best half member has been identified and discard rest members. Further, in current population nPso is employed to maintain exploration and exploitation, then nDE is used to enhance convergence accuracy. The proposed i h PsoDE and its integrating component nPso and nDE have been tested over 23 basic, 30 IEEE CEC2014 and 30 IEEE CEC2017 unconstrained benchmark functions plus 3 real life optimization problems. The performance of proposed algorithms compared with traditional Pso and DE, their existed variants/hybrids as well as some of the other state-of-the-art algorithms. The results indicate the superiority of proposed algorithms. Finally, based on overall performance i h PsoDE is recommended for bound-unconstrained optimization problems in this present study.
Rueymaw Chen - One of the best experts on this subject based on the ideXlab platform.
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particle swarm optimization with justification and designed mechanisms for resource constrained project scheduling problem
Expert Systems With Applications, 2011Co-Authors: Rueymaw ChenAbstract:Research highlights? This study generated schedules by both forward and backward scheduling particle swarms. ? This work applies justification technique to further shorten the makespan of the yield schedule. ? To synchronize the justified schedules, a mapping scheme is adopted to modify the particle. ? To enhance the performance, the latest finish time heuristic is used in particles' initialization. The studied resource-constrained project scheduling problem (RCPSP) is a classical well-known problem which involves resource, precedence, and temporal constraints and has been applied to many applications. However, the RCPSP is confirmed to be an NP-hard combinatorial problem. Restated, it is hard to be solved in a reasonable time. Therefore, there are many metaheuristics-based schemes for finding near optima of RCPSP were proposed. The particle swarm optimization (Pso) is one of the metaheuristics, and has been verified being an efficient nature-inspired algorithm for many optimization problems. For enhancing the Pso efficiency in solving RCPSP, an effective scheme is suggested. The justification technique is combined with Pso as the proposed justification particle swarm optimization (JPso), which includes other designed mechanisms. The justification technique adjusts the start time of each activity of the yielded schedule to further shorten the makespan. Moreover, schedules are generated by both forward scheduling particle swarm and backward scheduling particle swarm in this work. Additionally, a mapping scheme and a modified communication mechanism among particles with a designed gbest ratio (GR) are also proposed to further improve the efficiency of the proposed JPso. Simulation results demonstrate that the proposed JPso provides an effective and efficient approach for solving RCPSP.
