Abstract: In this paper, we presented a Multi-Objective Random
Drift Particle Swarm Optimization algorithm (MORDPSO-CD) based
on RDPSO and crowding distance sorting to improve the convergence
and distribution with less computation cost. MORDPSO-CD makes
the most of RDPSO to approach the true Pareto optimal solutions
fast. We adopt the crowding distance sorting technique to update and
maintain the archived optimal solutions. Introducing the crowding
distance technique into MORDPSO can make the leader particles
find the true Pareto solution ultimately. The simulation results reveal
that the proposed algorithm has better convergence and distribution.
Abstract: Automation of airport operations can greatly improve
ground movement efficiency. In this paper, we study the speed profile
design problem for advanced airport ground movement control and
guidance. The problem is constrained by the surface four-dimensional
trajectory generated in taxi planning. A decomposed approach of two
stages is presented to solve this problem efficiently. In the first stage,
speeds are allocated at control points, which ensure smooth speed
profiles can be found later. In the second stage, detailed speed profiles
of each taxi interval are generated according to the allocated control
point speeds with the objective of minimizing the overall fuel
consumption. We present a swarm intelligence based algorithm for the
first-stage problem and a discrete variable driven enumeration method
for the second-stage problem, since it only has a small set of discrete
variables. Experimental results demonstrate the presented
methodology performs well on real world speed profile design
problems.
Abstract: Waste load allocation (WLA) policies may use multiobjective
optimization methods to find the most appropriate and
sustainable solutions. These usually intend to simultaneously
minimize two criteria, total abatement costs (TC) and environmental
violations (EV). If other criteria, such as inequity, need for
minimization as well, it requires introducing more binary
optimizations through different scenarios. In order to reduce the
calculation steps, this study presents value index as an innovative
decision making approach. Since the value index contains both the
environmental violation and treatment costs, it can be maximized
simultaneously with the equity index. It implies that the definition of
different scenarios for environmental violations is no longer required.
Furthermore, the solution is not necessarily the point with minimized
total costs or environmental violations. This idea is testified for Haraz
River, in north of Iran. Here, the dissolved oxygen (DO) level of river
is simulated by Streeter-Phelps equation in MATLAB software. The
WLA is determined for fish farms using multi-objective particle
swarm optimization (MOPSO) in two scenarios. At first, the trade-off
curves of TC-EV and TC-Inequity are plotted separately as the
conventional approach. In the second, the Value-Equity curve is
derived. The comparative results show that the solutions are in a
similar range of inequity with lower total costs. This is due to the
freedom of environmental violation attained in value index. As a
result, the conventional approach can well be replaced by the value
index particularly for problems optimizing these objectives. This
reduces the process to achieve the best solutions and may find better
classification for scenario definition. It is also concluded that decision
makers are better to focus on value index and weighting its contents
to find the most sustainable alternatives based on their requirements.
Abstract: This paper presents an algorithm of particle swarm
optimization with reduction for global optimization problems. Particle
swarm optimization is an algorithm which refers to the collective
motion such as birds or fishes, and a multi-point search algorithm
which finds a best solution using multiple particles. Particle
swarm optimization is so flexible that it can adapt to a number
of optimization problems. When an objective function has a lot of
local minimums complicatedly, the particle may fall into a local
minimum. For avoiding the local minimum, a number of particles are
initially prepared and their positions are updated by particle swarm
optimization. Particles sequentially reduce to reach a predetermined
number of them grounded in evaluation value and particle swarm
optimization continues until the termination condition is met. In order
to show the effectiveness of the proposed algorithm, we examine the
minimum by using test functions compared to existing algorithms.
Furthermore the influence of best value on the initial number of
particles for our algorithm is discussed.
Abstract: This paper proposes a new version of the Particle
Swarm Optimization (PSO) namely, Modified PSO (MPSO) for
model order formulation of Single Input Single Output (SISO) linear
time invariant continuous systems. In the General PSO, the
movement of a particle is governed by three behaviors namely
inertia, cognitive and social. The cognitive behavior helps the
particle to remember its previous visited best position. In Modified
PSO technique split the cognitive behavior into two sections like
previous visited best position and also previous visited worst
position. This modification helps the particle to search the target very
effectively. MPSO approach is proposed to formulate the higher
order model. The method based on the minimization of error
between the transient responses of original higher order model and
the reduced order model pertaining to the unit step input. The results
obtained are compared with the earlier techniques utilized, to validate
its ease of computation. The proposed method is illustrated through
numerical example from literature.
Abstract: An Optimal Power Flow based on Improved Particle
Swarm Optimization (OPF-IPSO) with Generator Capability Curve
Constraint is used by NN-OPF as a reference to get pattern of
generator scheduling. There are three stages in Designing NN-OPF.
The first stage is design of OPF-IPSO with generator capability curve
constraint. The second stage is clustering load to specific range and
calculating its index. The third stage is training NN-OPF using
constructive back propagation method. In training process total load
and load index used as input, and pattern of generator scheduling
used as output. Data used in this paper is power system of Java-Bali.
Software used in this simulation is MATLAB.
Abstract: This paper presents a new approach using Combined Artificial Neural Network (CANN) module for daily peak load forecasting. Five different computational techniques –Constrained method, Unconstrained method, Evolutionary Programming (EP), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) – have been used to identify the CANN module for peak load forecasting. In this paper, a set of neural networks has been trained with different architecture and training parameters. The networks are trained and tested for the actual load data of Chennai city (India). A set of better trained conventional ANNs are selected to develop a CANN module using different algorithms instead of using one best conventional ANN. Obtained results using CANN module confirm its validity.
Abstract: A self-evolution algorithm for optimizing neural networks using a combination of PSO and JPSO is proposed. The algorithm optimizes both the network topology and parameters simultaneously with the aim of achieving desired accuracy with less complicated networks. The performance of the proposed approach is compared with conventional back-propagation networks using several synthetic functions, with better results in the case of the former. The proposed algorithm is also implemented on slope stability problem to estimate the critical factor of safety. Based on the results obtained, the proposed self evolving network produced a better estimate of critical safety factor in comparison to conventional BPN network.