14,060 research outputs found
A review of velocity-type PSO variants
This paper presents a review of the particular variants of particle swarm optimization, based on the velocity-type class.
The original particle swarm optimization algorithm was developed as an unconstrained optimization technique, which
lacks a model that is able to handle constrained optimization problems. The particle swarm optimization and its
inapplicability in constrained optimization problems are solved using the dynamic-objective constraint-handling
method. The dynamic-objective constraint-handling method is originally developed for two variants of the basic particle
swarm optimization, namely restricted velocity particle swarm optimization and self-adaptive velocity particle swarm
optimization. Also on the subject velocity-type class, a review of three other variants is given, specifically: (1) vertical
particle swarm optimization; (2) velocity limited particle swarm optimization; and (3) particle swarm optimization with
scape velocity. These velocity-type particle swarm optimization variants all have in common a velocity parameter which
determines the direction/movements of the particles.info:eu-repo/semantics/publishedVersio
A survey of swarm intelligence for dynamic optimization: algorithms and applications
Swarm intelligence (SI) algorithms, including ant colony optimization, particle swarm optimization, bee-inspired algorithms, bacterial foraging optimization, firefly algorithms, fish swarm optimization and many more, have been proven to be good methods to address difficult optimization problems under stationary environments. Most SI algorithms have been developed to address stationary optimization problems and hence, they can converge on the (near-) optimum solution efficiently. However, many real-world problems have a dynamic environment that changes over time. For such dynamic optimization problems (DOPs), it is difficult for a conventional SI algorithm to track the changing optimum once the algorithm has converged on a solution. In the last two decades, there has been a growing interest of addressing DOPs using SI algorithms due to their adaptation capabilities. This paper presents a broad review on SI dynamic optimization (SIDO) focused on several classes of problems, such as discrete, continuous, constrained, multi-objective and classification problems, and real-world applications. In addition, this paper focuses on the enhancement strategies integrated in SI algorithms to address dynamic changes, the performance measurements and benchmark generators used in SIDO. Finally, some considerations about future directions in the subject are given
A hybrid EKF and switching PSO algorithm for joint state and parameter estimation of lateral flow immunoassay models
This is the post-print version of the Article. The official published can be accessed from the link below - Copyright @ 2012 IEEEIn this paper, a hybrid extended Kalman filter (EKF) and switching particle swarm optimization (SPSO) algorithm is proposed for jointly estimating both the parameters and states of the lateral flow immunoassay model through available short time-series measurement. Our proposed method generalizes the well-known EKF algorithm by imposing physical constraints on the system states. Note that the state constraints are encountered very often in practice that give rise to considerable difficulties in system analysis and design. The main purpose of this paper is to handle the dynamic modeling problem with state constraints by combining the extended Kalman filtering and constrained optimization algorithms via the maximization probability method. More specifically, a recently developed SPSO algorithm is used to cope with the constrained optimization problem by converting it into an unconstrained optimization one through adding a penalty term to the objective function. The proposed algorithm is then employed to simultaneously identify the parameters and states of a lateral flow immunoassay model. It is shown that the proposed algorithm gives much improved performance over the traditional EKF method.This work was supported in part by the International Science and Technology Cooperation Project of China under Grant
2009DFA32050, Natural Science Foundation of China under Grants 61104041, International Science and Technology Cooperation Project of Fujian Province of China under Grant
2009I0016
Hybridization of multi-objective deterministic particle swarm with derivative-free local searches
The paper presents a multi-objective derivative-free and deterministic global/local hybrid algorithm for the efficient and effective solution of simulation-based design optimization (SBDO) problems. The objective is to show how the hybridization of two multi-objective derivative-free global and local algorithms achieves better performance than the separate use of the two algorithms in solving specific SBDO problems for hull-form design. The proposed method belongs to the class of memetic algorithms, where the global exploration capability of multi-objective deterministic particle swarm optimization is enriched by exploiting the local search accuracy of a derivative-free multi-objective line-search method. To the authors best knowledge, studies are still limited on memetic, multi-objective, deterministic, derivative-free, and evolutionary algorithms for an effective and efficient solution of SBDO for hull-form design. The proposed formulation manages global and local searches based on the hypervolume metric. The hybridization scheme uses two parameters to control the local search activation and the number of function calls used by the local algorithm. The most promising values of these parameters were identified using forty analytical tests representative of the SBDO problem of interest. The resulting hybrid algorithm was finally applied to two SBDO problems for hull-form design. For both analytical tests and SBDO problems, the hybrid method achieves better performance than its global and local counterparts
Driving dynamic multi-objective optimizations constrained by decision-makers' preferences
Dissertation (MSc (Computer Science)) -- University of Pretoria, 2019.Dynamic multi-objective optimization problems (DMOOPs) are an interesting and
a relatively complex class of optimization problems where elements of the problems,
such as objective functions and/or constraints, change with time. These problems are
characterized with at least two objective functions in con
ict with one another. Sometimes,
human decision-makers seek to in
uence ways (by restricting the search to a
specific region of the Pareto-optimal Front (POF)) in which algorithms that optimize
these problems behave by incorporating personal preferences into the optimization process.
This dissertation proposes approaches that enable decision-makers to in
uence the
optimization process with their preferences. The decision-makers' imparted preferences
force a reformulation of the optimization problems as constrained problems, where the
constraints are defined in the objective space. Consequently, the constrained problems
are then solved using variations of constraint handling techniques, such as penalization
of infeasible solutions and the restriction of the search to the feasible region. The proposed
algorithmic approaches' performance are compared using standard performance
measures for dynamic multi-objective optimization (DMOO) and newly proposed measures.
The proposed measures estimate how well an algorithm is able to find solutions
in the objective space that best re
ect the decision-maker's preferences and the paretooptimality
goal of DMOO. This dissertation also proposes a new di erential evolution
algorithm, called dynamic di erential evolution vector-evaluated non-dominated sorting
(2DEVENS). 2DEVENS combines elements of the dynamic non-dominated sort genetic
algorithm version II (DNSGA-II) and the dynamic vector-evaluated particle swarm optimization
(DVEPSO) algorithm to drive the search for solutions.
The proposed 2DEVENS algorithm compared favorably with other nature-inspired
algorithms that were used in the studies carried out for this dissertation. The proposed
approaches used in incorporating decision-makers' preferences in the optimization
process also demonstrated good results.Computer ScienceMSc (Computer Science)Unrestricte
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
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