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Adaptive Inertia Weight Particle Swarm Optimization
2006Adaptive inertia weight is proposed to rationally balance the global exploration and local exploitation abilities for particle swarm optimization. The resulting algorithm is called adaptive inertia weight particle swarm optimization algorithm (AIW-PSO) where a simple and effective measure, individual search ability (ISA), is defined to indicate whether
Zheng Qin +3 more
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Adaptive Inertia-Weighted Firefly Algorithm
2019Real-life optimization problems required more and more technique, which completely utilizes the search spaces to obtain the best optimal solution, so researchers have an opportunity to propose a new technique or a modified version of the existing technique.
Shailja Sharma, Pooja Jain, Akash Saxena
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Self Inertia Weight Adaptation for the Particle Swarm Optimization
Proceedings of the International Conference on Learning and Optimization Algorithms: Theory and Applications, 2018Particle swarm optimization is a stochastic population-based metaheuristic algorithm, it been successful in solving a height range of real-world problems. The primary challenge present in PSO is to balance between global and local search during the optimization process. The classical PSO algorithm applies the same search scheme for all particles, which
Oussama Aoun +2 more
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Improved DPSO Algorithm with Dynamically Changing Inertia Weight
2015Population Diversity in Particle Swarm Optimization DPSO algorithm can effectively balance the "exploration" and "exploitation" ability of the PSO optimization algorithm and improve the optimization accuracy and stability of standard PSO algorithm. However, the accuracy of DPSO for solving the multi peak function will be obviously decreased.
Jing Xin, Cuicui Yan, Xiangshuai Han
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Exponential Inertia Weight in Particle Swarm Optimization
2016This paper presents an improved particle swarm optimization algorithm (EWPSO) with a novel strategy for inertia weight. In the new algorithm, nonlinear inertia weight is proposed. The new weight is an exponential function of the minimal and maximal fitness of the particles in each iteration. The set of benchmark function was used to test the new method.
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A Modified Particle Swarm Optimizer with Dynamical Inertia Weight
2009To increase the convergence speed and prevent the prematurity of the particle swarm optimizer (PSO), a novel strategy for inertia weight was proposed, which was different from the traditional linearly decreasing weight (LDW). The inertia weight was dynamically updated by two factors (the dispersion degree and advance degree factors) which have ...
Ai-min Miao +4 more
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On adaptive chaotic inertia weights in Particle Swarm Optimization
2013 IEEE Symposium on Swarm Intelligence (SIS), 2013Inertia weight is one of the control parameters that influence the performance of Particle Swarm Optimization (PSO). Since the introduction of the inertia weight parameter into PSO technique, different inertia weight strategies have been proposed to enhance the performance of PSO in handling optimization problems.
Akugbe Martins Arasomwan +1 more
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On the relation between the positive inertia index and negative inertia index of weighted graphs
Linear Algebra and its Applications, 2019zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Shuchao Li, Wanting Sun
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A decreasing inertia weight particle swarm optimizer
Engineering Optimization, 2007It has been over ten years since the pioneering work of particle swarm optimization (PSO) espoused by Kennedy and Eberhart. Since then, various modifications, well suited to particular application areas, have been reported widely in the literature.
Shu-Kai S. Fan, Yi-Yin Chiu
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Particle Swarm Optimization with Ensemble of Inertia Weight Strategies
2017Particle swarm optimization (PSO) has gained significant attention for solving numerical optimization problems in different applications. However, the performance of PSO depends on the appropriate setting of inertia weight and the optimal setting changes with generations during the evolution. Therefore, different adaptive inertia weight strategies have
Muhammad Zeeshan Shirazi +3 more
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