An adaptive invasion-based model for distributed Differential Evolution

Falco, Ivanoe De; Cioppa, Antonio Della; Maisto, Domenico; Scafuri, Umberto; Tarantino, Ernesto

doi:10.1016/j.ins.2014.03.083

Cited by 23 publications

(7 citation statements)

References 46 publications

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“…A common question when optimization algorithms are proposed is the computational complexity (also called computational cost or effort, CE) [9]. Usually, the CE of swarm methods like PSO is measured using the number of objective function calls.…”

Section: Proposed Methods -[I]psomentioning

confidence: 99%

An interval space reducing method for constrained problems with particle swarm optimization

Machado-Coelho

Machado

Jaulin

et al. 2017

Applied Soft Computing

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In this paper, we propose a method for solving constrained optimization problems using Interval Analysis combined with Particle Swarm Optimization. A Set Inverter Via Interval Analysis algorithm is used to handle constraints in order to reduce constrained optimization to quasi unconstrained one. The algorithm is useful in the detection of empty search spaces, preventing useless executions of the optimization process. To improve computational efficiency, a Space Cleaning algorithm is used to remove solutions that are certainly not optimal. As a result, the search space becomes smaller at each step of the optimization procedure. After completing pre-processing, a modified Particle Swarm Optimization algorithm is applied to the reduced search space to find the global optimum. The efficiency of the proposed approach is demonstrated through comprehensive experimentation involving 100,000 runs on a set of wellknown benchmark constrained engineering design problems. The computational efficiency of the new method is quantified by comparing its results with other PSO variants found in the literature.

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Section: Proposed Methods -[I]psomentioning

confidence: 99%

An interval space reducing method for constrained problems with particle swarm optimization

Machado-Coelho

Machado

Jaulin

et al. 2017

Applied Soft Computing

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“…The influence of synchronous and asynchronous migration on the performance of adaptive DE algorithms has been also investigated in Bujok (2013). In De Falco et al (2014) a novel migration model is implemented through a multistage process that involves invading subpopulations and their competition with native individuals. Recently, in Kozlov et al (2016), a parallel technique called DEEP (Differential Evolution Entirely Parallel) has been implemented and publicly released.…”

Section: Related Workmentioning

confidence: 99%

Towards cloud-based parallel metaheuristics

Teijeiro

Pardo

González

et al. 2016

The International Journal of High Performance Computing Applica

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Many key problems in science and engineering can be formulated and solved using global optimization techniques. In the particular case of computational biology, the development of dynamic (kinetic) models is one of the current key issues. In this context, the problem of parameter estimation (model calibration) remains as a very challenging task. The complexity of the underlying models requires the use of efficient solvers to achieve adequate results in reasonable computation times. Metaheuristics have been the focus of great consideration as an efficient way of solving hard global optimization problems. Even so, in most realistic applications, metaheuristics require a very large computation time to obtain an acceptable result. Therefore, several parallel schemes have been proposed, most of them focused on traditional parallel programming interfaces and infrastructures. However, with the emergence of cloud computing, new programming models have been proposed to deal with large-scale data processing on clouds. In this paper we explore the applicability of these new models for global optimization problems using as a case study a set of challenging parameter estimation problems in systems biology. We have developed, using Spark, an island-based parallel version of Differential Evolution. Differential Evolution is a simple population-based metaheuristic that, at the same time, is very popular for being very efficient in real function global optimization. Several experiments were conducted both on a cluster and on the Microsoft Azure public cloud to evaluate the speedup and efficiency of the proposal, concluding that the Spark implementation achieves not only competitive speedup against the serial implementation, but also good scalability when the number of nodes grows. The results can be useful for those interested in using parallel metaheuristics for global optimization problems benefiting from the potential of new cloud programming models.

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“…In the short period since the emergence of the coronavirus, there have been several mathematical models [5–17], to name a few. Some of these models attempt to evaluate the contribution from different transmission routes, such as contact tracing and isolation [10, 11, 18], travel restrictions [16, 17] social distancing [19, 20], lock-down measures [16, 21, 22], and a combination of these interventions [12, 23–26]. These models broadly fall into three categories, deterministic, stochastic, and simulations.…”

Section: Introductionmentioning

confidence: 99%

“…Several new mathematical techniques used in different disciplines have been employed to gain insights, which would not be possible with the traditional approaches in the field. These include the human mobility model [16], differential evolution[19], heuristic optimization technique [19], stochastic agent-based discrete time simulation [27], supply chain risk simulations [28], etc.…”

Section: Introductionmentioning

confidence: 99%

A reductive analysis of a compartmental model for COVID-19: data assimilation and forecasting for the United Kingdom

Ananthakrishna

Kumar

2020

Preprint

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We introduce a deterministic model that partitions the total population into the susceptible, infected, quarantined, and those traced after exposure, recovered and the deceased. We introduce the concept of 'accessible population for transmission of the disease', which can be a small fraction of the total population, for instance when interventions are in force. This assumption, together with the structure of the set of coupled nonlinear ordinary differential equations for the populations, allows us to decouple the equations into just two equations. This further reduces to a logistic type of equation for the total infected population. The equation can be solved analytically and therefore allows for a clear interpretation of the growth and inhibiting factors in terms of the parameters in the full model. The validity of the 'accessible population' assumption and the efficacy of the reduced logistic model is demonstrated by the ease of fitting the United Kingdom data for the total number of infected cases. The model can also be used to forecast further progression of the disease. The approach further helps us to analyze the original model equations. We show that the original model equations provide a very good fit with the United Kingdom data for the cumulative number of infections. The active infected population of the model is seen to exhibit a turning point around mid-May, suggesting the beginning of a slow-down in the spread of infections. However, the rate of slowing down beyond the turning point is small and therefore the cumulative number of infections is likely to saturate to about 3.8 × 105 only towards the end of July or beginning of August, provided the lock-down conditions continue to prevail. Noting that the fits obtained from the reduced logistic equation and the full model equations are equally good, the underlying causes for the limited forecasting ability of the reduced logistic equation is elucidated. The model and the procedure adopted here are expected to be useful in fitting the data for other countries and forecasting the progression of the disease.

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An adaptive invasion-based model for distributed Differential Evolution

Cited by 23 publications

References 46 publications

An interval space reducing method for constrained problems with particle swarm optimization

An interval space reducing method for constrained problems with particle swarm optimization

Towards cloud-based parallel metaheuristics

A reductive analysis of a compartmental model for COVID-19: data assimilation and forecasting for the United Kingdom

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