On the efficiency of the binary flower pollination algorithm: Application on the antenna positioning problem

PurposePrevious evidence suggests that glioma re-resection can be effective in improving clinical outcomes. Furthermore, the use of mapping techniques during surgery has proven beneficial for newly diagnosed glioma patients. However, the effects of these mapping techniques during re-resection are not clear. This systematic review aimed to assess the evidence of using these techniques for recurrent glioma patients.MethodsA systematic search was performed to identify relevant studies. Articles were eligible if they included adult patients with recurrent gliomas (WHO grade 2–4) who underwent re-resection. Study characteristics, application of mapping, and surgical outcome data on survival, patient functioning, and complications were extracted.ResultsThe literature strategy identified 6372 articles, of which 125 were screened for eligibility. After full-text evaluation, 58 articles were included in this review, comprising 5311 patients with re-resection for glioma. Of these articles, 17% (10/58) reported the use of awake or asleep intraoperative mapping techniques during re-resection. Mapping was applied in 5% (280/5311) of all patients, and awake craniotomy was used in 3% (142/5311) of the patients.ConclusionMapping techniques can be used during re-resection, with some evidence that it is useful to improve clinical outcomes. However, there is a lack of high-quality support in the literature for using these techniques. The low number of studies reporting mapping techniques may, next to publication bias, reflect limited application in the recurrent setting. We advocate for future studies to determine their utility in reducing morbidity and increasing extent of resection, similar to their benefits in the primary setting.

The purpose of this study is to develop a control system that optimizes the phase plan, sequence and signal timing using the flower pollination algorithm (FPA). At the same time, it is aimed to improve the fixed-time control system with the optimum cycle length search approach based on the differential evolution algorithm. The applicability and performances of these two control systems were examined in 15 different traffic situations according to 4 different intersection geometries. Fixed-time and optimized fuzzy logic traffic controller (FLC) developed by Dogan were used as the reference control systems in performance comparison. The optimum cycle length search system can achieve approximately 18% improvement over the fixed-time system, but showed lower performance than the FPA and FLC control systems. The FPA system has proven its applicability by achieving the best performance with about a 30% improvement compared to the fixed-time system and about 3% improvement compared to the FLC system. The FPA approach, which has a fast and effective performance, has been found to be an alternative method for intersection control, and it is foreseen that it can increase the intersection capacity and reduce the negative effects such as delay and fuel consumption.

Binary knapsack problem has received considerable attention in combinational optimization. Various meta-heuristic algorithms are dedicated to solve this problem in the literature. Recently, a binary flower pollination algorithm (BFPA) was proposed, which has been successfully applied to solve 0-1 knapsack problem. In this paper, two new time-varying transfer functions are proposed to improve the exploration and exploitation capability of the BFPA with the best solution and short computing time. Based on small, medium, and high-dimensional scales of the knapsack problem, the computational results reveal that the proposed time-varying transfer functions not only to find the best possible solutions but also to have less computational time. Compared to the standard transfer functions, the efficiency of the proposed time-varying transfer functions is superior, especially in the high-dimensional scales.

A species-based flower pollination algorithm with increased selection pressure in abiotic local pollination and enhanced intensification

This article presents a study on the optimization of parameters of a solid oxide fuel cell (SOFC) by using seven different metaheuristic-based optimization techniques such as Differential Evolutionary algorithm (DE), Genetic Algorithm (GA), Evolutionary Programming (EP), Flower Pollination Algorithm (FPA), Evolutionary Strategies (ES), Memetic Algorithm (MA) and Coral Reefs Optimization (CRO). Optimization of a solid oxide fuel cell which is a complex, non-linear and multimodal problem, is solved by using these algorithms. Voltage-based objective function expressed as minimization of the sum of squared error has been developed in this study. Based on given optimization algorithms, the best value of unknown parameters of solid oxide fuel cell has been reported. Current–voltage, power and efficiency curves are shown in this study to match the estimated values with experimental values. Differential evolutionary algorithm (DE) is found to be best evolutionary algorithm having sum of squared error, mean and standard deviation of 8.33E-06, 1.57E-05 and 6.792E-06 respectively which are minimum among all other algorithm. Differential evolutionary algorithm predicts accurately among all other algorithms, used to find the unknown parameters of solid oxide fuel cell. Box plot shows the minimum median and lowest IQR which further validate the authenticity of DE among all evolutionary based algorithms to find unknown parameters of SOFC used in this study.

A self-adaptive binary differential evolution algorithm for large scale binary optimization problems

Due to its NP-hard nature, it is still difficult to find an optimal solution for instances of the binary knapsack problem as small as 100 variables. In this paper, we developed a three-level hyper-heuristic framework to generate algorithms for the problem. From elementary components and multiple sets of problem instances, algorithms are generated. The best algorithms are selected to go through a second step process, where they are evaluated with problem instances that differ in size and difficulty. The problem instances are generated according to methods that are found in the literature. In all of the larger problem instances, the generated algorithms have less than 1 % error with respect to the optimal solution. Additionally, generated algorithms are efficient, taking on average fractions of a second to find a solution for any instance, with a standard deviation of 1 s. In terms of structure, hyper-heuristic algorithms are compact in size compared with those in the literature, allowing an in-depth analysis of their structure and their presentation to the scientific world.

Unwanted convergence to a local optimum, rather than global optimum, is possible to take place in practical multimodal optimization problems. Communication between artificial agents in the stochastic algorithms is one of the solutions to this issue. This paper proposes a novel parallel optimization algorithm, namely FDA, based on the communication of the pollen in Flower pollination algorithm (FPA) with the agents in Differential evolution algorithm (DEA) to solve the optimization problems. A communication strategy for Pollens and Agents is to take advantages of the strength points of each algorithm to explore and exploit the diversity solutions in avoiding of dropping to a local optimum. A set of benchmark functions is used to test the quality performance of the proposed algorithm. Simulation results show that the proposed algorithm in-creases the accuracy more than the existing algorithms.

Visual shape matching has been a hot research topic. As a relatively new branch, atomic potential matching (APM) model is inspired by potential field attractions. Compared to the conventional edge potential function (EPF) model, APM has been verified to be less sensitive to intricate backgrounds in the test image and far more cost-effective in the computation process. The optimization process of shape matching can be regarded as a numerical optimization problem, which is disposed by flower pollination algorithm (FPA). This study comprehensively investigates the convergence performances of FPA and the other algorithms in shape matching problem based on APM model. Experimental results of three realistic examples show that FPA is able to provide very competitive results and to outperform the other algorithms.

Differential Evolution (DE) is a simple and fast population based Nature Inspired Algorithm (NIA). When it is experimented for benchmark problems as well as real world problems it outperformed other population based NIAs and other search metaheuristics in terms of rate of convergence. But it is not an exception in class of population based algorithms, it also show premature convergence and stagnates at suboptimal points. Therefore this paper tries to sustain a better convergence speed and keep away from stagnation by introducing levy flight random search process along with opposition based learning strategy in original DE algorithm. The proposed algorithm named as Opposition Based Levy Flight Search in DE (OLFDE) Algorithm. The newly proposed algorithm OLFDE tested over fourteen benchmark problems and three real world problems (Pressure Vessel Design, Parameter estimation for frequency modulated sound wave and Compression Spring problem) in order to prove its efficiency over some recent variants of DE.

Continuous optimization is one of the most active research lines in evolutionary and metaheuristic algorithms. Since CEC 2005 and CEC 2008 competitions, many different algorithms have been proposed to solve continuous problems. Despite there exist very good algorithms reporting high quality results for a given dimension, the scalability of the search methods is still an open issue. Finding an algorithm with competitive results in the range of 50 to 500 dimensions is a difficult achievement. This contribution explores the use of a hybrid memetic algorithm based on the differential evolution algorithm, named MDE-DC. The proposed algorithm combines the explorative/exploitative strength of two heuristic search methods, that separately obtain very competitive results in either low or high dimensional problems. This paper uses the benchmark problems and conditions required for the workshop on “evolutionary algorithms and other metaheuristics for Continuous Optimization Problems – A Scalability Test” chaired by Francisco Herrera and Manuel Lozano.

DisABC: A new artificial bee colony algorithm for binary optimization

Binary metaheuristic algorithms for 0–1 knapsack problems: Performance analysis, hybrid variants, and real-world application

A large population island framework for the unconstrained binary quadratic problem

With the rapid development of the Internet of Things (IoT), the curse of dimensionality becomes increasingly common. Feature selection (FS) is to eliminate irrelevant and redundant features in the datasets. Particle swarm optimization (PSO) is an efficient metaheuristic algorithm that has been successfully applied to obtain the optimal feature subset with essential information in an acceptable time. However, it is easy to fall into the local optima when dealing with high-dimensional datasets due to constant parameter values and insufficient population diversity. In the paper, an FS method is proposed by utilizing adaptive PSO with leadership learning (APSOLL). An adaptive updating strategy for parameters is used to replace the constant parameters, and the leadership learning strategy is utilized to provide valid population diversity. Experimental results on 10 UCI datasets show that APSOLL has better exploration and exploitation capabilities through comparison with PSO, grey wolf optimizer (GWO), Harris hawks optimization (HHO), flower pollination algorithm (FPA), salp swarm algorithm (SSA), linear PSO (LPSO), and hybrid PSO and differential evolution (HPSO-DE). Moreover, less than 8% of features in the original datasets are selected on average, and the feature subsets are more effective in most cases compared to those generated by 6 traditional FS methods (analysis of variance (ANOVA), Chi-Squared (CHI2), Pearson, Spearman, Kendall, and Mutual Information (MI)).