The construction site layout (CSL) design presents a particularly interesting area of study because of its relatively high level of attention to usability qualities, in addition to common engineering objectives such as cost and performance. However, it is difficult combinatorial optimization problem for engineers. Swarm intelligence (SI) was very popular and widely used in many complex optimiza...

In this paper, a new approach is proposed for the optimum design of single-phase induction motor. By using the classical design equations and the evolutionary algorithms such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO) and Modified Particle Swarm Optimization (MPSO), a Single Phase Induction Motor (SPIM) was designed with the maximum efficiency. The Finite Element Method (FEM)...

Particle Swarm Optimization (PSO) is a relatively recent heuristic search method whose mechanics are inspired by the swarming or collaborative behavior of biological populations. PSO is similar to the Genetic Algorithm (GA) in the sense that these two evolutionary heuristics are population-based search methods. In other words, PSO and the GA move from a set of points (population) to another set...

The near net shaped manufacturing ability of squeeze casting process requiresto set the process variable combinations at their optimal levels to obtain both aesthetic appearance and internal soundness of the cast parts. The aesthetic and internal soundness of cast parts deal with surface roughness and tensile strength those can readily put the part in service without the requirement of costly s...

In this paper, a novel intelligent optimization algorithm Artificial Tribe Algorithm (ATA) is presented based on the analyses of the principle and uniform framework of the Bionic Intelligent Optimization Algorithms (BIOA). ATA simulates the existent skills of the natural tribes, and actualizes the optimization purpose through the propagation and migration behaviors of the tribes. The main facto...

In this paper we investigate the application of the Particle Swarm Optimization (PSO) technique for solving the Hardware/Software partitioning problem. The PSO is attractive for the Hardware/Software partitioning problem as it offers reasonable coverage of the design space together with O(n) main loop's execution time, where n is the number of proposed solutions that will evolve to provide the ...

Particle Swarm Optimization (PSO) is a popular algorithm used extensively in continuous optimization. One of its well-known drawbacks is its propensity for premature convergence. Many techniques have been proposed for alleviating this problem. One of the popular and promising approaches is low-level hybridization (LLH) of PSO with Genetic Algorithm (GA). Nevertheless, the LLH implementation is ...

This chapter presents the application of a comprehensive statistical analysis for both algorithmic performance comparison and optimal parameter estimation on a multi-objective digital signal processing problem. The problem of designing optimum digital finite impulse response (FIR) filters with the simultaneous approximation of the filter magnitude and phase is posed as a multiobjective optimiza...

Among variety of meta-heuristic population-based search algorithms, particle swarm optimization (PSO) with adaptive inertia weight (AIW) has been considered as a versatile optimization tool, which incorporates the experience of the whole swarm into the movement of particles. Although the exploitation ability of this algorithm is great, it cannot comprehensively explore the search space and may ...