Abstract
In this article, in order to enhance the rate of convergence and scattering of particles at the same time, simple techniques are introduced. These techniques include: (1) Using the interval search to select a new particle candidate, (2) Replacement of three candidate particles instead to worst the particles in the population, (3) Using the best result of learning coefficients, (4) using a simple method to control the convergence of the algorithm in a high number of repetitions. In this article, the performance of Quantum-Behaved Particle Swarm Optimization(QPSO) algorithm has been upgraded with using the interval search method. The proposed method of interval search of quantum-behaved particle swarm optimization algorithm has achieved better results than in the past with the use of quadratic interpolation recombination operator and stable deviation and interval search. Moreover, the results of the proposed algorithm of Interval Search with Quadratic Interpolation and Stable Deviation Quantum-Behaved Particle Swarm Optimization (IQS-QPSO) is compared with the other former algorithms such as Quantum-Behaved Particle Swarm Optimization (QPSO), Quadratic Interpolation Quantum-Behaved Particle Swarm Optimization (Q-QPSO) and Stable Deviation Quantum-Behaved Particle Swarm Optimization (SD-QPSO). Then the performance improvement is reported. In order to compare the results of each algorithm, five famous functions are used and consequently the results are reported separately for each function
Highlights
Optimizing is a methodical knowledge, with the ability to find out the optimal solution between all possible solutions for a problem
After presenting the IQS-Quantum Particle Swarm Optimization (QPSO) algorithm, it is applied on five test functions
The results obtained from this algorithm are compared with the former algorithms such as QPSO, QQPSO and SDQPSO on basis of different population size ranging from 20 to 200, different generation ranging from 20 to 1500 and different dimensions ranging from 11 to 20
Summary
Optimizing is a methodical knowledge, with the ability to find out the optimal solution between all possible solutions for a problem. The objective function is the range including acceptable solutions This range is determined by the constraints that must be accurately determined by use of equal or unequal signs mathematically. The best position achieved by each particle is called personal experience, which is simultaneously recorded This experience in relation with the part or the whole population will determine the willingness of a group to move in a specific direction. Development of particle swarm optimization is based on rules and concepts of organized societies in nature such as bird migration, movement of fish and flock of animals. In recent years, this method has been widely studied. The results show that the proposed method could be compared with other smart direct search algorithms such as genetic algorithm [5,6]
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