In the context of spectrum surveillance, a new method to recover the code of spread spectrum signal is presented, while the receiver has no knowledge of the transmitter’s spreading sequence. In our previous paper, we used Genetic algorithm (GA), to recover spreading code. Although genetic algorithms (GAs) are well known for their robustness in solving complex optimization problems, but nonethel...

In recent days, Swarm Intelligence plays an important role in solving many real life optimization problems. Particle Swarm Optimization (PSO) is swarm intelligence based search and optimization algorithm which is used to solve global optimization problems. But due to lack of population diversity and premature convergence it is often trapped into local optima. We can increase diversity and preve...

This chapter presents particle swarm optimization (PSO) based algorithms. After an overview of PSO’s development and application history also two application examples are given in the following. PSO’s robustness and its simple applicability without the need for cumbersome derivative calculations make it an attractive optimization method. Such features also allow this algorithm to be adjusted fo...

The permutation flow shop scheduling problem (PFSSP) is part of production scheduling, which belongs to the hardest combinatorial optimization problem. In this paper, a multipopulation particle swarm optimization (PSO) based memetic algorithm (MPSOMA) is proposed in this paper. In the proposed algorithm, the whole particle swarm population is divided into three subpopulations in which each part...

Keywords: PSO QPSO Mean best position Weight parameter WQPSO a b s t r a c t Quantum-behaved particle swarm optimization (QPSO) algorithm is a global convergence guaranteed algorithms, which outperforms original PSO in search ability but has fewer parameters to control. In this paper, we propose an improved quantum-behaved particle swarm optimization with weighted mean best position according t...

In Evolutionary Algorithms (EA), elites are crucial to maintain good features in solutions. However, too many elites could make the evolutionary process stagnate and cannot enhance the performance. In this paper, we employ Particle Swarm Optimization (PSO) and Biogeography-Based Optimization (BBO) to propose a hybrid algorithm termed Biogeography-Based Particle Swarm Optimization (BPSO) which c...

This paper presents Chemo-tactic PSO-DE (CPSO-DE) optimization algorithm combined with Lagrange Relaxation method (LR) for solving Unit Commitment (UC) problem. The proposed approach employs Chemo-tactic PSO-DE algorithm for optimal settings of Lagrange multipliers. It provides high-quality performance and reaches global solution and is a hybrid heuristic algorithm based on Bacterial Foraging O...

By taking advantage of niche sharing scheme,we propose a novel co-evolutionary particle swarm optimization algorithm (NCPSO) to solve permutation flow shop scheduling problem. As the core of this algorithm, niche sharing scheme maximizes the diversity of population and hence improves the quality of individuals. To evaluate the performance of the proposed algorithm, we have use eight Taillard in...

-Aiming at the shortcoming of basic PSO algorithm, that is, easily trapping into local minimum, we propose an advanced PSO algorithm with SA and apply this new algorithm for solving TSP problem. The core of algorithm is based on the PSO algorithm. SA method is used to slow down the degeneration of the PSO swarm and increase the swarm’s diversity. The comparative experiments were made between PS...

To overcome the challenges of Data gathering and enhancement of lifetime of mobile nodes we propose a new data gathering technique with multiple mobile sinks based on particle swarm optimization (PSO) technique. Particle changes its condition according to the following three principles in the basic particle swarm optimization algorithm: (1) to keep its inertia (2) to change the condition accord...