Inertia Weight strategies in Particle Swarm Optimization
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Cites background from "Inertia Weight strategies in Partic..."
...Using the inertia weight, the contribution ratio of the previous velocity of a particle to its velocity at the current time stage is determined [62]....
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"Inertia Weight strategies in Partic..." refers background in this paper
...In 1998, first time Shi and Eberhart [2] presented the concept of Inertia Weight by introducing Constant Inertia Weight....
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...Further, the concept of an Inertia Weight was developed by Shi and Eberhart [2] in 1998 to better control exploration and exploitation....
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"Inertia Weight strategies in Partic..." refers background in this paper
...Linear Decreasing Inertia Weight * #,4 #,4 # / 5 " #,4 6 7 [6]...
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...The Linearly Decreasing strategy [6] enhances the efficiency and performance of PSO....
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Related Papers (5)
Frequently Asked Questions (9)
Q2. What is the main idea of the paper?
In order to overcome the premature convergence and later period oscillatory occurrences of the standard PSO, an Exponent Decreasing Inertia Weight and a stochastic mutation to produce an improved PSO has been proposed by Gao et al. [12] which uses the Exponent Decreasing Inertia Weight along with stochastic piecewise mutation for current global optimal particle during the running time, thus strengthened jumped out the partial optimal solution ability.
Q3. What is the title of the paper?
Fayek et al. [11] introduces an optimized Particle Swarm technique (PSOSA) that uses Simulated Annealing for optimizing the Inertia Weight and tested the approach on urban planning problem.
Q4. What is the purpose of the article?
To overcome the weakness of premature convergence to local minimum, Adaptive Inertia Weight strategy [4] is proposed to improve its searching capability.
Q5. What is the termination criterion for the inertia weight strategy?
The termination criterion is set to the “no improvement observed for 200 iterations (similar fitness value achieved for 200 consecutive iterations)”.
Q6. What is the way to optimize the inertia weight?
Oscillating Inertia Weight [8] provides periodically alternates between global and local search waves and conclusion was drawn that this strategy appears to be generally competitive and, in some cases, outperform particularly in terms of convergence speed.
Q7. What is the IEEE'08 Third World Congress on Nature and Biologically Inspired Computing?
Y. Gao, X. An, and J. Liu., “A Particle Swarm Optimization Algorithm with Logarithm Decreasing Inertia Weight and Chaos Mutation”, In Computational Intelligence and Security, 2008.
Q8. What is the difference between the two?
So they combine sigmoid function and Linear Increasing Inertia Weight and provides a SIIW which has produced a great improvement in quick convergence ability and aggressive movement narrowing towards the solution region.
Q9. what is the weight strategy for a test?
2011 Third World Congress on Nature and Biologically Inspired Computing 643TABLE 3. AVERAGE ERROR VALUE OF DIFFERENT INERTIA WEIGHT STRATEGIES FOR DIFFERENT TEST PROBLEMSProblemInertia Weight StrategySphere Griewank Rosenbrock Rastrigin AckleyConstant 0 0.0660 87.1177 0.9959 3.76E-15Random 16.2943 84.7315 49419.74 99.8390 18.4242Adaptive 5.1986 16.5427 4525.81 77.6964 13.3161Sigmoid increasing 6.6605 29.4044 3138.98 61.6105 13.6855Sigmoid decreasing 28.7272 83.9782 11677.4 85.4881 18.1589Linear decreasing 7.01E-81 0.0691 6.1676 39.7121 2.94E-15Chaotic 5.48E-81 0.0913 3.6398 3.2203 3.41E-15Chaotic random 15.6258 91.7888 68234.573 85.3247 17.7203Oscillating 0 0.0562 232.9622 2.8883 3.76E-15Global-local best 19.8213 54.2368 67725.4 78.9104 17.9878Simulated annealing 0 0.0669 44.077 4.183 2.94E-15Natural exponent (e1-PSO) 0 0.0752 4.7199 2.7223 3.05E-15Natural exponent (e2-PSO) 5.0885 30.6310 631.2029 31.8677 12.1880Logarithm decreasing 2.34E-36 0.0859 3.7097 4.9466 4.36E-15Exponent decreasing 0 0.0717 3.5584 3.6519 2.94E-15TABLE