6,782 research outputs found
Particle Swarm Optimization: A survey of historical and recent developments with hybridization perspectives
Particle Swarm Optimization (PSO) is a metaheuristic global optimization
paradigm that has gained prominence in the last two decades due to its ease of
application in unsupervised, complex multidimensional problems which cannot be
solved using traditional deterministic algorithms. The canonical particle swarm
optimizer is based on the flocking behavior and social co-operation of birds
and fish schools and draws heavily from the evolutionary behavior of these
organisms. This paper serves to provide a thorough survey of the PSO algorithm
with special emphasis on the development, deployment and improvements of its
most basic as well as some of the state-of-the-art implementations. Concepts
and directions on choosing the inertia weight, constriction factor, cognition
and social weights and perspectives on convergence, parallelization, elitism,
niching and discrete optimization as well as neighborhood topologies are
outlined. Hybridization attempts with other evolutionary and swarm paradigms in
selected applications are covered and an up-to-date review is put forward for
the interested reader.Comment: 34 pages, 7 table
A Logic-Based Mixed-Integer Nonlinear Programming Model to Solve Non-Convex and Non-Smooth Economic Dispatch Problems: An Accuracy Analysis
This paper presents a solver-friendly logic-based mixed-integer nonlinear
programming model (LB-MINLP) to solve economic dispatch (ED) problems
considering disjoint operating zones and valve-point effects. A simultaneous
consideration of transmission losses and logical constraints in ED problems
causes difficulties either in the linearization procedure, or in handling via
heuristic-based approaches, and this may result in outcome violation. The
non-smooth terms can make the situation even worse. On the other hand,
non-convex nonlinear models with logical constraints are not solvable using the
existing nonlinear commercial solvers. In order to explain and remedy these
shortcomings, we proposed a novel recasting strategy to overcome the hurdle of
solving such complicated problems with the aid of the existing nonlinear
solvers. The proposed model can facilitate the pre-solving and probing
techniques of the commercial solvers by recasting the logical constraints into
the mixed-integer terms of the objective function. It consequently results in a
higher accuracy of the model and better computational efficiency. The acquired
results demonstrated that the LB-MINLP model, compared to the existing
(heuristic-based and solver-based) models in the literature, can easily handle
the non-smooth and nonlinear terms and achieve an optimal solution much faster
and without any outcome violation
A novel metaheuristic method for solving constrained engineering optimization problems: Drone Squadron Optimization
Several constrained optimization problems have been adequately solved over
the years thanks to advances in the metaheuristics area. In this paper, we
evaluate a novel self-adaptive and auto-constructive metaheuristic called Drone
Squadron Optimization (DSO) in solving constrained engineering design problems.
This paper evaluates DSO with death penalty on three widely tested engineering
design problems. Results show that the proposed approach is competitive with
some very popular metaheuristics.Comment: 3 page
Improvement of PSO algorithm by memory based gradient search - application in inventory management
Advanced inventory management in complex supply chains requires effective and
robust nonlinear optimization due to the stochastic nature of supply and demand
variations. Application of estimated gradients can boost up the convergence of
Particle Swarm Optimization (PSO) algorithm but classical gradient calculation
cannot be applied to stochastic and uncertain systems. In these situations
Monte-Carlo (MC) simulation can be applied to determine the gradient. We
developed a memory based algorithm where instead of generating and evaluating
new simulated samples the stored and shared former function evaluations of the
particles are sampled to estimate the gradients by local weighted least squares
regression. The performance of the resulted regional gradient-based PSO is
verified by several benchmark problems and in a complex application example
where optimal reorder points of a supply chain are determined.Comment: book chapter, 20 pages, 7 figures, 2 table
A hybrid GA–PS–SQP method to solve power system valve-point economic dispatch problems
This study presents a new approach based on a hybrid algorithm consisting of Genetic Algorithm (GA), Pattern Search (PS) and Sequential Quadratic Programming (SQP) techniques to solve the well-known power system Economic dispatch problem (ED). GA is the main optimizer of the algorithm, whereas PS and SQP are used to fine tune the results of GA to increase confidence in the solution. For illustrative purposes, the algorithm has been applied to various test systems to assess its effectiveness. Furthermore, convergence characteristics and robustness of the proposed method have been explored through comparison with results reported in literature. The outcome is very encouraging and suggests that the hybrid GA–PS–SQP algorithm is very efficient in solving power system economic dispatch problem
Diversity Enhancement for Micro-Differential Evolution
The differential evolution (DE) algorithm suffers from high computational
time due to slow nature of evaluation. In contrast, micro-DE (MDE) algorithms
employ a very small population size, which can converge faster to a reasonable
solution. However, these algorithms are vulnerable to a premature convergence
as well as to high risk of stagnation. In this paper, MDE algorithm with
vectorized random mutation factor (MDEVM) is proposed, which utilizes the small
size population benefit while empowers the exploration ability of mutation
factor through randomizing it in the decision variable level. The idea is
supported by analyzing mutation factor using Monte-Carlo based simulations. To
facilitate the usage of MDE algorithms with very-small population sizes, new
mutation schemes for population sizes less than four are also proposed.
Furthermore, comprehensive comparative simulations and analysis on performance
of the MDE algorithms over various mutation schemes, population sizes, problem
types (i.e. uni-modal, multi-modal, and composite), problem dimensionalities,
and mutation factor ranges are conducted by considering population diversity
analysis for stagnation and trapping in local optimum situations. The studies
are conducted on 28 benchmark functions provided for the IEEE CEC-2013
competition. Experimental results demonstrate high performance and convergence
speed of the proposed MDEVM algorithm.Comment: Developed version is submitted for review to Applied soft computin
Improved dynamical particle swarm optimization method for structural dynamics
A methodology to the multiobjective structural design of buildings based on an improved particle swarm optimization algorithm is presented, which has proved to be very efficient and robust in nonlinear problems and when the optimization objectives are in conflict. In particular, the behaviour of the particle swarm optimization (PSO) classical algorithm is improved by dynamically adding autoadaptive mechanisms that enhance the exploration/exploitation trade-off and diversity of the proposed algorithm, avoiding getting trapped in local minima. A novel integrated optimization system was developed, called DI-PSO, to solve this problem which is able to control and even improve the structural behaviour under seismic excitations. In order to demonstrate the effectiveness of the proposed approach, the methodology is tested against some benchmark problems. Then a 3-story-building model is optimized under different objective cases, concluding that the improved multiobjective optimization methodology using DI-PSO is more efficient as compared with those designs obtained using single optimization.Peer ReviewedPostprint (published version
A Social Spider Algorithm for Solving the Non-convex Economic Load Dispatch Problem
Economic Load Dispatch (ELD) is one of the essential components in power
system control and operation. Although conventional ELD formulation can be
solved using mathematical programming techniques, modern power system
introduces new models of the power units which are non-convex,
non-differentiable, and sometimes non-continuous. In order to solve such
non-convex ELD problems, in this paper we propose a new approach based on the
Social Spider Algorithm (SSA). The classical SSA is modified and enhanced to
adapt to the unique characteristics of ELD problems, e.g., valve-point effects,
multi-fuel operations, prohibited operating zones, and line losses. To
demonstrate the superiority of our proposed approach, five widely-adopted test
systems are employed and the simulation results are compared with the
state-of-the-art algorithms. In addition, the parameter sensitivity is
illustrated by a series of simulations. The simulation results show that SSA
can solve ELD problems effectively and efficiently
Fish School Search Algorithm for Constrained Optimization
In this work we investigate the effectiveness of the application of niching
able swarm metaheuristic approaches in order to solve constrained optimization
problems. Sub-swarms are used in order to allow the achievement of many
feasible regions to be exploited in terms of fitness function. The niching
approach employed was wFSS, a version of the Fish School Search algorithm
devised specifically to deal with multi-modal search spaces. A base technique
referred as wrFSS was conceived and three variations applying different
constraint handling procedures were also proposed. Tests were performed in
seven problems from CEC 2010 and a comparison with other approaches was carried
out. Results show that the search strategy proposed is able to handle some
heavily constrained problems and achieve results comparable to the
state-of-the-art algorithms. However, we also observed that the local search
operator present in wFSS and inherited by wrFSS makes the fitness convergence
difficult when the feasible region presents some specific geometrical features
PSO and CPSO Based Interference Alignment for K-User MIMO Interference Channel
This paper investigates how to use a metaheuristic based technique, namely
Particle Swarm Optimization (PSO), in carrying out of Interference Alignment
(IA) for -User MIMO Interference Channel (IC). Despite its increasing
popularity, mainly in wireless communications, IA lacks of explicit and
straightforward design procedures. Indeed, IA design results in complex
optimization tasks involving a large amount of decision variables, together
with a problem of convergence of the IA solutions. In this paper the IA
optimization is performed using PSO and Cooperative PSO (CPSO) more suitable
for large scale optimization, a comparison between the two versions is also
carried out. This approach seems to be promising.Comment: 9 pages, 3 figures. arXiv admin note: text overlap with
arXiv:1710.0086
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