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Particle swarm optimization (PSO) is a stochastic computational technique for finding optimal regions of complex search spaces. The method was inspired by social behavior of organism and it belongs to the evolutionary computation (EC) to the group of swarm intelligence (SI). It has a few parameters to adjust and the method is easy to implement, which are the main advantages. The main aim of this paper is to find out, if limitation of the search space influences the optimization process. In this paper, we compare two modifications of PSO algorithm - variant using the parameter of inertia weight and variant with constriction factor. Both variants are solved separately for constrained and unconstrained space. Constrained area is when the boundaries are clear district, unconstrained is when particles can cross the boundaries. For comparison, eleven benchmark functions which were prepared for the special session on real-parameter optimization of CEC 2005 were used. Differences in results between constrained and unconstrained area is presented in this paper. It was found, that constrained area of search space gives better results. In additional, variant of PSO algorithm with linearly decreasing inertia weigh is better for chosen optimization problem. [ABSTRACT FROM AUTHOR]

This paper speaks to the changed Tropos objective model for handling the danger related with the product advancement cycle. Here the modification is done by the hybrid optimization algorithm. The tropos model will be improved by selecting the optimal candidate solution by using Hybrid Gravitational Search Algorithm – Particle Swarm Optimization algorithm. The proposed approach characterizes a strategy to dissect the relationship between the hubs of each layer to assess their odds of raising the dangers. Here the risks are prioritized and which are identified by the improved Tropos method. The obtained results show that, the proposed optimization-based goal model has delivered better performance in terms of time and memory requirement when compared with the normal model and also the risk and cost of the proposed model is proved to be more efficient than existing model. [ABSTRACT FROM AUTHOR]

In view of the needs of the vehicle unit, this paper has carried on the research to the sensor distribution question. In the case of fully taking into account the information such as target identity and target priority, the mathematical model of optimal sensor allocation is established based on the maximum information gain criterion, and then the population-based incremental learning algorithm is used to solve. Finally, the validity of the method is verified by solving the problem of sensor assignment problem of the vehicle unit. [ABSTRACT FROM AUTHOR]

This paper presents a new hybrid optimization algorithm to be used in feature selection problem. The proposed algorithm utilizes the power of sine cosine algorithm and Cuckoo search to both exploration and exploitation the search space to reach at best promising solution. The empirical results are evaluated on four datasets, named Breast, Leukemia, Heart and Iris that obtained from UCI repository and compared with the native sine cosine and other feature selection algorithms include Particle swarm optimization and Antlion algorithm. The results of the experiments clearly showed the efficiency of the hybrid algorithm to exploration and exploitation the features space for finest features and improved high classification rate with low run time on all used datasets. [ABSTRACT FROM AUTHOR]

In the analysis of geotechnical engineering problems, limit equilibrium methods are often engaged to analyze the forces and model the problem. On the other hand, the rapid development of computing technology has equipped a large number of methods to be employed in engineering problems, as well as geotechnical issues. These enhancement consequences the popularity of analytical limit equilibrium methods based on computer aid process. Finding the most critical slip surface is a challenging global optimization problem that is always improved by the latest problem solving procedures. The recent advances in Artificial Intelligence (AI) methods are making it possible to incorporate advance learning capability into finding procedure of this unique surface. The Particle Swarm Optimization (PSO) is a suitable search method for optimizing the non-linear functions which employs the concept of bird flock or fish school to gain this goal. This paper utilizes a PSO algorithm in a limit equilibrium slope stability analysis based on the Simplified Bishop Method to find the most critical circular slip surface of a homogenous slope as a real case study. Results show that the applied technique is able to find the critical slip surface with a reasonable accuracy. [ABSTRACT FROM AUTHOR]

Here we proposed a solution for the flow shop scheduling problems in a lot streaming with the intention of diminishing the Makespan. This particle swarm optimization algorithm is summarized here. The method has been used for a collection of different issues and the efficiency is calculated by comparing the results with other literatures. Finally, we concluded that the proposed developed algorithm works best when a local search is applied. [ABSTRACT FROM AUTHOR]

The social behavior of groups of birds, ants, insects and fish has been used to develop evolutionary algorithms known as swarm intelligence techniques for solving optimization problems. This work presents the development of strategies for the application of two of the popular swarm intelligence techniques, namely the particle swarm and ant colony methods, for the solution of multiobjective optimization problems. In a multiobjective optimization problem, the objectives exhibit a conflicting nature and hence no design vector can minimize all the objectives simultaneously. The concept of Pareto-optimal solution is used in finding a compromise solution. A modified cooperative game theory approach, in which each objective is associated with a different player, is used in this work. The applicability and computational efficiencies of the proposed techniques are demonstrated through several illustrative examples involving unconstrained and constrained problems with single and multiple objectives and continuous and mixed design variables. The present methodologies are expected to be useful for the solution of a variety of practical continuous and mixed optimization problems involving single or multiple objectives with or without constraints. [ABSTRACT FROM AUTHOR]