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Hybridizing particle swarm optimization (PSO) with differential evolution (DE), this paper proposes an integrated PSO–DE optimizer and examines the performance of this optimizer. Firstly, a new self-adaptive PSO (SAPSO) is establ...
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Hybridizing particle swarm optimization (PSO) with differential evolution (DE), this paper proposes an integrated PSO–DE optimizer and examines the performance of this optimizer. Firstly, a new self-adaptive PSO (SAPSO) is established to guide movements of particles in the proposed hybrid PSO. Aiming at well trade-offing the global and local search capabilities, a self-adaptive strategy is proposed to adaptively update the three main control parameters of particles in SAPSO. Since the performance of PSO heavily relies on its convergence, the convergence of SAPSO is analytically investigated and a convergence-guaranteed parameter selection rule is provided for SAPSO in this study. Subsequently, a modified self-adaptive differential evolution is presented to evolve the personal best positions of particles in the proposed hybrid PSO in order to mitigant the potential stagnation issue. Next, the performance of the proposed method is validated via 25 benchmark test functions and two real-world problems. The simulation results confirm that the proposed method performs significantly better than its peers at a confidence level of 95% over the 25 benchmarks in terms of the solution optimality. Besides, the proposed method outperforms its contenders over the majority of the 25 benchmarks with respect to the search reliability and the convergence speed. Moreover, the computational complexity of the proposed method is comparable with those of some other enhanced PSO–DE methods compared. The simulation results over the two real-world issues reveal that the proposed method dominates its competitors as far as the solution optimality is considered.
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SLSL-QPSO is a software that can find the optimal value of a function. It improves over the Quantum-behaved Particle Swarm Optimization (QPSO) algorithms by leveraging the concept of living and death as swarm layers like the param...
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SLSL-QPSO is a software that can find the optimal value of a function. It improves over the Quantum-behaved Particle Swarm Optimization (QPSO) algorithms by leveraging the concept of living and death as swarm layers like the parameter optimization in the Optimized PSO (OPSO) but without the super swarm, the Lévy mutation, the scoped contradiction-expansion coefficient, and the selection of effective layers. Experimental results demonstrate that SLSL-QPSO has superior performance in finding better optimal than QPSO and several other variants thus providing a competitive solution to optimization problems.
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This paper proposes a cellular particle swarm optimization (CPSO), hybridizing cellular automata (CA) and particle swarm optimization (PSO) for function optimization. In the proposed CPSO, a mechanism of CA is integrated in the ve...
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This paper proposes a cellular particle swarm optimization (CPSO), hybridizing cellular automata (CA) and particle swarm optimization (PSO) for function optimization. In the proposed CPSO, a mechanism of CA is integrated in the velocity update to modify the trajectories of particles to avoid being trapped in the local optimum. With two different ways of integration of CA and PSO, two versions of CPSO, i.e. CPSO-inner and CPSO-outer, have been discussed. For the former, we devised three typical lattice structures of CA used as neighborhood, enabling particles to interact inside the swarm; and for the latter, a novel CA strategy based on "smart-cell" is designed, and particles employ the information from outside the swarm. Theoretical studies are made to analyze the convergence of CPSO, and numerical experiments are conducted to compare the proposed algorithm with different variants of PSO. According to the experimental results, the proposed method performs better than other variants of PSO on benchmark test functions.
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Nature is the rich principal source for developing optimization algorithms. Metaheuristic algorithms can be classified with the emphasis on the source of inspiration into several categories such as biology, physics, and chemistry....
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Nature is the rich principal source for developing optimization algorithms. Metaheuristic algorithms can be classified with the emphasis on the source of inspiration into several categories such as biology, physics, and chemistry. The particle swarm optimization (PSO) is one of the mostwell-known bio-inspired optimization algorithms which mimics movement behavior of animal flocks especially bird and fish flocking. In standard PSO, velocity of each particle is influenced by the best individual and its best personal experience. This approach could make particles trap into the local optimums and miss opportunities of jumping to far better optimums than the currents and sometimes causes fast premature convergence. To overcome this issue, a new movement concept, so called extraordinariness particle swarm optimizer (EPSO) is proposed in this paper. The main contribution of this study is proposing extraordinary motion for particles in the PSO. Indeed, unlike predefined movement used in the PSO, particles in the EPSO can move toward a target which can be global best, local bests, or even the worst individual. The proposed improved PSO outperforms than the standard PSO and its variants for benchmarks such as CEC 2015 benchmarks. In addition, several constrained and engineering design problems have been tackled using the improved PSO and the optimization results have been compared with the standard PSO, variants of PSO, and other optimizers. (C) 2016 Elsevier B.V. All rights reserved.
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A lot of research has been directed to the new optimizers that can find a suboptimal solution for any optimization problem named as heuristic black-box optimizers. They can find the suboptimal solutions of an optimization problem ...
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A lot of research has been directed to the new optimizers that can find a suboptimal solution for any optimization problem named as heuristic black-box optimizers. They can find the suboptimal solutions of an optimization problem much faster than the mathematical programming methods (if they find them at all). Particle swarm optimization (PSO) is an example of this type. In this paper, a new modified PSO has been proposed. The proposed PSO incorporates conditional learning behavior among birds into the PSO algorithm. Indeed, the particles, little by little, learn how they should behave in some similar conditions. The proposed method is named Conditionalized Particle Swarm Optimization (CoPSO). The problem space is first divided into a set of subspaces in CoPSO. In CoPSO, any particle inside a subspace will be inclined towards its best experienced location if the particles in its subspace have low diversity; otherwise, it will be inclined towards the global best location. The particles also learn to speed-up in the non-valuable subspaces and to speed-down in the valuable subspaces. The performance of CoPSO has been compared with the state-of-the-art methods on a set of standard benchmark functions.
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Dynamic deployment methods for wireless sensor network (WSN) can improve the quality of service (QoS) of the network by adjusting positions of mobile nodes. In the dynamic deployment problem model of this paper, not only the cover...
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Dynamic deployment methods for wireless sensor network (WSN) can improve the quality of service (QoS) of the network by adjusting positions of mobile nodes. In the dynamic deployment problem model of this paper, not only the coverage rate of WSN but also the moving distance of mobile nodes is taken into consideration. This kind of model can be abstracted into multi-objective optimization problem, and particle swarm optimization (PSO) is introduced to solve this problem. In this paper, combined with previous work, an improved dynamic deployment method is proposed based on multi-swarm PSO. Specifically, we propose a discrete PSO to calculate the distance of mobile solutions, and a multi-swarm PSO is designed to optimize network performance for enhancing the QoS of deployment which includes higher coverage rate and lower energy consumption of mobile nodes. Experimental results demonstrate that the proposed method has a good performance in solving the WSN deployment problem.
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Many real-world optimization problems have to be treated as multi-objective optimization problems. An approach, well established in recent years, is to find Pareto optimal configurations of the trial variables by detecting nondomi...
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Many real-world optimization problems have to be treated as multi-objective optimization problems. An approach, well established in recent years, is to find Pareto optimal configurations of the trial variables by detecting nondominated solutions with the help of a suitable vector optimization method. Alternatively, relying on scalar optimization methods (both stochastic or deterministic), a suitable objective function taking all objectives into account simultaneously has to be defined. Depending on the number of trial variables, a scalar objective function of that type will exhibit a considerable number of feasible local solutions besides the global one. Therefore, a useful scalar optimization strategy should be able to end up (with a high probability) in the best of all possible solutions in the given search space and additionally detect as many local solutions as possible. Some population-based stochastic methods are implicitly suited for that task; others can be enhanced to fulfill these requirements. Higher order evolution strategies have successfully been tuned for that kind of problem by introducing cluster sensitive recombination [niching higher order evolution strategy (NES)]. The firefly algorithm (FFA) mimics the behavior of fireflies, which use a kind of flashing light to communicate with other members of their species. Since the intensity of the light of a single firefly diminishes with increasing distance, the FFA is implicitly able to detect local solutions on its way to the best solution for a given scalar objective function. The FFA will be applied to the well-known Rastrigin test function and to a shielding/shunting electromagnetic problem with two and three objectives, respectively, and its results will be compared with the ones obtained with an NES.
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