DEVELOPMENT OF A MODIFIED PARTICLE SWARM OPTIMIZATION BASED CULTURAL ALGORITHM FOR SOLVING UNIVERSITY TIMETABLING PROBLEM

Timetabling problems are search problems in which courses must be arranged around a set of timeslots so that some constraints are satisfied. However, slow convergence speed and high computational complexity are one of drawbacks limiting the efficiency of the existing timetabling algorithms. In this paper, a Modified Particle Swarm Optimization based Cultural Algorithm which is characterized with low computational complexity and high convergence speed was developed for solving university lecture timetabling problems. The standard Particle Swarm Optimization (PSO) algorithm was modified by introducing influence factors and acceleration component in order to improve the converge speed of the algorithm. Cultural algorithm was formulated by incorporating the Modified Particle Swarm Optimization (MPSO) into its population space. Thus, the developed Modified Particle Swarm Optimization based Cultural Algorithm could be implemented and employed for solving lecture timetabling problems in higher institutions

Integration of Genetic Algorithm and Cultural Particle Swarm Algorithms for Constrained Optimization of Industrial Organization and Diffusion Efficiency Analysis in Equipment Manufacturing Industry

Aiming at industrial organization multi-objective optimization problem in Equipment Manufacturing Industry, The paper proposes a new type of double layer evolutionary cultural particle swarm optimization algorithm. The algorithm combines the advantages of the particle swarm optimization algorithm and cultural algorithm. It not only revises the problem that the particles are easy to "premature", but also overcomes the drawback of penalty function method. Firstly, improved topology structure of Particle swarm optimization algorithm. Secondly, using crossover strategy and niche competition mechanism. Verified by the test functions, the proposed algorithm has good performance. Through the analysis of the manufacturing performance based on the algorithm, the paper proposes some optimization strategies such as improving the manufacturing industry market concentration, improving the manufacturing level of industry product differentiation and so on

Integration of Genetic Algorithm and Cultural Particle Swarm Algorithms for Constrained Optimization of Industrial Organization and Diffusion Efficiency Analysis in Equipment Manufacturing Industry

Abstract: Aiming at industrial organization multi-objective optimization problem in Equipment Manufacturing Industry, The paper proposes a new type of double layer evolutionary cultural particle swarm optimization algorithm. The algorithm combines the advantages of the particle swarm optimization algorithm and cultural algorithm. It not only revises the problem that the particles are easy to "premature", but also overcomes the drawback of penalty function method. Firstly, improved topology structure of Particle swarm optimization algorithm. Secondly, using crossover strategy and niche competition mechanism. Verified by the test functions, the proposed algorithm has good performance. Through the analysis of the manufacturing performance based on the algorithm, the paper proposes some optimization strategies such as improving the manufacturing industry market concentration, improving the manufacturing level of industry product differentiation and so on

Implementation of a Simplified Cultural-Based Multi-Objective Particle Swarm Optimization

This paper presents a simplified Cultural based Multi-Objective Particle Swarm Optimization (MOPSO) algorithm. In this algorithm we modify momentum and global acceleration components of the conventional MOPSO algorithm. The algorithm has been tested on common benchmark functions. Its performance has been compared with other algorithms, using standard test metrics. The results show that the cultural based MOPSO is more efficient and robust

A Novel Cultural Quantum-behaved Particle Swarm Optimization Algorithm

kai.zenger @ aalto.fi A novel cultural quantum-behaved particle swarm optimization algorithm (CQPSO) is proposed to improve the performance of the quantum-behaved PSO (QPSO). The cultural framework is embedded in the QPSO, and the knowledge stored in the belief space can guide the evolution of the QPSO. 15 high-dimensional and multi-modal functions are employed to investigate the proposed algorithm. Numerical simulation results demonstrate that the CQPSO can indeed outperform the QPSO

Adaptive Rule-Base Influence Function Mechanism for Cultural Algorithm

This study proposes a modified version of cultural algorithms (CAs) which benefits from rule-based system for influence function. This rule-based system selects and applies the suitable knowledge source according to the distribution of the solutions. This is important to use appropriate influence function to apply to a specific individual, regarding to its role in the search process. This rule based system is optimized using Genetic Algorithm (GA). The proposed modified CA algorithm is compared with several other optimization algorithms including GA, particle swarm optimization (PSO), especially standard version of cultural algorithm. The obtained results demonstrate that the proposed modification enhances the performance of the CA in terms of global optimality.Optimization is an important issue in different scientific applications. Many researches dedicated to algorithms that can be used to find an optimal solution for different applications. Intelligence optimizations which are generally classified as, evolutionary computations techniques like Genetic Algorithm, evolutionary strategy, and evolutionary programming, and swarm intelligence algorithms like particle swarm intelligence algorithm and ant colony optimization, etc are powerful tools for solving optimization problem