Combined quantum particle swarm optimization algorithm for multi-objective nutritional diet decision making

The Combinatorial problems are real world decision making problem with discrete and disjunctive choices. When these decision making problems involve more than one conflicting objective and constraint, it turns the polynomial time problem into NP-hard. Thus, the straight forward approaches to solve multi-objective problems would not give an optimal solution. In such case evolutionary based meta-heuristic approaches are found suitable. In this paper, a novel particle swarm optimization based meta-heuristic algorithm is presented to solve multi-objective combinatorial optimization problems. Here a mapping method is considered to convert the binary and discrete values (solution encoded as particles) to a continuous domain and update it using the velocity and position update equation of particle swarm optimization to find new set of solutions in continuous domain and demap it to discrete values. The performance of the algorithm is compared with other evolutionary strategy like SPEA and NSGA-II on pseudo-Boolean discrete problems and multi-objective 0/1 knapsack problem. The experimental results confirmed the better performance of combinatorial particle swarm optimization algorithm.

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Optimization of Neural Tree Based on an Improved Quantum Particle Swarm Optimization

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.475-476.956 ◽

2013 ◽

Vol 475-476 ◽

pp. 956-959 ◽

Cited By ~ 1

Author(s):

Hao Teng ◽

Shu Hui Liu ◽

Yue Hui Chen

Keyword(s):

Particle Swarm Optimization ◽

Optimization Algorithm ◽

Particle Swarm Optimization Algorithm ◽

Quantum Particle ◽

Particle Swarm ◽

Optimal Solution ◽

Particle Swarm Algorithm ◽

Swarm Optimization ◽

Quantum Particle Swarm Optimization ◽

Local Optimal Solution

In the model of flexible neural tree (FNT), parameters are usually optimized by particle swarm optimization algorithm (PSO). Because PSO has many shortcomings such as being easily trapped in local optimal solution and so on, an improved algorithm based on quantum-behaved particle swarm optimization (QPSO) is presented. It is combined with the factor of speed, gather and disturbance, so as to be used to optimize the parameters of FNT. This paper applies the improved quantum particle swarm optimization algorithm to the neural tree, and compares it with the standard particle swarm algorithm in the optimization of FNT. The result shows that the proposed algorithm is with a better expression, thus improves the performance of the FNT.

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Quantum Particle Swarm Optimization Algorithm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.63-64.106 ◽

2011 ◽

Vol 63-64 ◽

pp. 106-110 ◽

Cited By ~ 6

Author(s):

Yu Fa Xu ◽

Jie Gao ◽

Guo Chu Chen ◽

Jin Shou Yu

Keyword(s):

Particle Swarm Optimization ◽

Quantum Theory ◽

Optimization Algorithm ◽

Particle Swarm Optimization Algorithm ◽

Quantum Particle ◽

Particle Swarm ◽

Experimental Results ◽

Swarm Optimization ◽

Local Optima ◽

Quantum Particle Swarm Optimization

Based on the problem of traditional particle swarm optimization (PSO) easily trapping into local optima, quantum theory is introduced into PSO to strengthen particles’ diversities and avoid the premature convergence effectively. Experimental results show that this method proposed by this paper has stronger optimal ability and better global searching capability than PSO.

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