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Feature selection is an important step for data pre-processing specially in the case of high dimensional data set. Performance of the data model is reduced if the model is trained with high dimensional data set, and it results in poor classification accuracy. Therefore, before training the model an important step to apply is the feature selection on the dataset to improve the performance and classification accuracy.

A novel optimization approach that hybridizes binary particle swarm optimization (BPSO) and differential evolution (DE) for fine tuning of SVM classifier is presented. The name of the implemented classifier is given as DEPSOSVM.

This approach is evaluated using 20 UCI benchmark text data classification data set. Further, the performance of the proposed technique is also evaluated on UCI benchmark image data set of cancer images. From the results, it can be observed that the proposed DEPSOSVM techniques have significant improvement in performance over other algorithms in the literature for feature selection. The proposed technique shows better classification accuracy as well.

The proposed approach is different from the previous work, as in all the previous work DE/(rand/1) mutation strategy is used whereas in this study DE/(rand/2) is used and the mutation strategy with BPSO is updated. Another difference is on the crossover approach in our case as we have used a novel approach of comparing best particle with sigmoid function. The core contribution of this paper is to hybridize DE with BPSO combined with SVM classifier (DEPSOSVM) to handle the feature selection problems.

The purpose of the study is distributed generation planning in a radial delivery framework to identify an appropriate location with a suitable rating of DG units energized by renewable energy resources to scale back the power loss and to recover the voltage levels. Though several algorithms have already been proposed through the target of power loss reduction and voltage stability enhancement, further optimization of the objectives is improved by using a combination of heuristic algorithms like DE and particle swarm optimization (PSO).

The identification of the candidate buses for the location of DG units and optimal rating of DG units is found by a combined differential evolution (DE) and PSO algorithm. In the combined strategy of DE and PSO, the key merits of both algorithms are combined. The DE algorithm prevents the individuals from getting trapped into the local optimum, thereby providing efficient global optimization. At the same time, PSO provides a fast convergence rate by providing the best particle among the entire iteration to obtain the best fitness value.

The proposed DE-PSO takes advantage of the global optimization of DE and the convergence rate of PSO. The different case studies of multiple DG types are carried out for the suggested procedure for the 33- and 69-bus radial delivery frameworks and a real 16-bus distribution substation in Tamil Nadu to show the effectiveness of the proposed methodology and distribution system performance. From the obtained results, there is a substantial decrease in the power loss and an improvement of voltage levels across all the buses of the system, thereby maintaining the distribution system within the framework of system operation and safety constraints.

A comparison of an equivalent system with the DE, PSO algorithm when used separately and other algorithms available in literature shows that the proposed method results in an improved performance in terms of the convergence rate and objective function values. Finally, an economic benefit analysis is performed if a photo-voltaic based DG unit is allocated in the considered test systems.

The purpose of this paper is to propose a multi-objective differential evolution algorithm named as MOMR-DE to resolve multicast routing problem. In mobile ad hoc network (MANET), multicast routing is a non-deterministic polynomial -complete problem that deals with the various objectives and constraints. Quality of service (QoS) in the multicast routing problem mainly depends on cost, delay, jitter and bandwidth. So the cost, delay, jitter and bandwidth are always considered as multi-objective for designing multicast routing protocols. However, mobile node battery energy is finite and the network lifetime depends on node battery energy. If the battery power consumption is high in any one of the nodes, the chances of network’s life reduction due to path breaks are also more. On the other hand, node’s battery energy had to be consumed to guarantee high-level QoS in multicast routing to transmit correct data anywhere and at any time. Hence, the network lifetime should be considered as one objective of the multi-objective in the multicast routing problem.

Recently, many metaheuristic algorithms formulate the multicast routing problem as a single-objective problem, although it obviously is a multi-objective optimization problem. In the MOMR-DE, the network lifetime, cost, delay, jitter and bandwidth are considered as five objectives. Furthermore, three QoS constraints which are maximum allowed delay, maximum allowed jitter and minimum requested bandwidth are included. In addition, we modify the crossover and mutation operators to build the shortest-path multicast tree to maximize network lifetime and bandwidth, minimize cost, delay and jitter.

Two sets of experiments are conducted and compared with other algorithms for these problems. The simulation results show that our proposed method is capable of achieving faster convergence and is more preferable for multicast routing in MANET.

In MANET, most metaheuristic algorithms formulate the multicast routing problem as a single-objective problem. However, this paper proposes a multi-objective differential evolution algorithm to resolve multicast routing problem, and the proposed algorithm is capable of achieving faster convergence and more preferable for multicast routing.

Impedance data obtained by electrochemical impedance spectroscopy (EIS) are fitted to a relevant electrical equivalent circuit to evaluate parameters directly related to the resistance and the durability of metal–coating systems. The purpose of this study is to present a novel and more efficient computational strategy for the modelling of EIS measurements using the Differential Evolution paradigm.

An alternative method to non-linear regression algorithms for the analysis of measured data in terms of equivalent circuit parameters is provided by evolutionary algorithms, particularly the Differential Evolution (DE) algorithms (standard DE and a representative of the self-adaptive DE paradigm were used).

The results obtained with DE algorithms were compared with those yielding from commercial fitting software, achieving a more accurate solution, and a better parameter identification, in all the cases treated. Further, an enhanced fitting power for the modelling of metal–coating systems was obtained.

The great potential of the developed tool has been demonstrated in the analysis of the evolution of EIS spectra due to progressive degradation of metal–coating systems. Open codes of the different differential algorithms used are included, and also, examples tackled in the document are open. It allows the complete use, or improvement, of the developed tool by researchers.

The purpose of this paper is to propose an algorithm that combines the particle swarm optimization (PSO) with the biogeography-based optimization (BBO) algorithm.

The BBO and the PSO algorithms are jointly used in to order to combine the advantages of both algorithms. The efficiency of the proposed algorithm is tested using some selected standard benchmark functions. The performance of the proposed algorithm is compared with that of the differential evolutionary (DE), genetic algorithm (GA), PSO, BBO, blended BBO and hybrid BBO-DE algorithms.

Experimental results indicate that the proposed algorithm outperforms the BBO, PSO, DE, GA, and the blended BBO algorithms and has comparable performance to that of the hybrid BBO-DE algorithm. However, the proposed algorithm is simpler than the BBO-DE algorithm since the PSO does not have complex operations such as mutation and crossover used in the DE algorithm.

The proposed algorithm is a generic algorithm that can be used to efficiently solve optimization problems similar to that solved using other popular evolutionary algorithms but with better performance.

The Wiener-Hammerstein nonlinear system is made up of two dynamic linear subsystems in series with a static nonlinear subsystem, and it is widely used in electrical, mechanical, aerospace and other fields. This paper considers the parameter estimation of the Wiener-Hammerstein output error moving average (OEMA) system.

The idea of multi-population and parameter self-adaptive identification is introduced, and a multi-population self-adaptive differential evolution (MPSADE) algorithm is proposed. In order to confirm the feasibility of the above method, the differential evolution (DE), the self-adaptive differential evolution (SADE), the MPSADE and the gradient iterative (GI) algorithms are derived to identify the Wiener-Hammerstein OEMA system, respectively.

From the simulation results, the authors find that the estimation errors under the four algorithms stabilize after 120, 30, 20 and 300 iterations, respectively, and the estimation errors of the four algorithms converge to 5.0%, 3.6%, 2.7% and 7.3%, which show that all four algorithms can identify the Wiener-Hammerstein OEMA system.

Compared with DE, SADE and GI algorithm, the MPSADE algorithm not only has higher parameter estimation accuracy but also has a faster convergence speed. Finally, the input–output relationship of laser welding system is described and identified by the MPSADE algorithm. The simulation results show that the MPSADE algorithm can effectively identify parameters of the laser welding system.

The purpose of this paper is to evaluate the performance of social spider algorithm (SSA) to solve constrained structural optimisation problems and to compare its results with others algorithms such as genetic algorithm, particle swarm optimisation, differential evolution and artificial bee colony.

To handle the constraints of the problems, this paper couples to the SSA an efficient selection criteria proposed in the literature that promotes a tournament between two solutions in which the feasible or less infeasible solution wins. The discussion is conducted on the competitiveness of the SSA with other algorithms as well as its performance in constrained problems.

SSA is a population algorithm proposed for global optimisation inspired by the foraging of social spiders. A spider moves on the web towards the position of the prey, guided by vibrations that occur around it in different frequencies. The SSA was proposed to solve problems without constraints, but these are present in most of practical problems. This paper evaluates the performance of SSA to solve constrained structural optimisation problems and compares its results with other algorithms such as genetic algorithm, particle swarm optimisation, differential evolution and artificial bee colony.

The proposed algorithm has no limitations, and it can be applied in other classes of constrained optimisation problems.

This paper evaluated the proposed algorithm with a benchmark of constrained structural optimisation problems intensely used in the literature, but it can be applied to solve real constrained optimisation problems in engineering and others areas.

This is the first paper to evaluate the performance of SSA in constrained problems and to compare its results with other algorithms traditional in the literature.

Vertical handoff mechanism (VHO) becomes very popular because of the improvements in the mobility models. These developments are less to certain circumstances and thus do not provide support in generic mobility, but the vertical handover management providing in the heterogeneous wireless networks (HWNs) is crucial and challenging. Hence, this paper introduces the vertical handoff management approach based on an effective network selection scheme.

This paper aims to improve the working principle of previous methods and make VHO more efficient and reliable for the HWN.Initially, the handover triggering techniques is modelled for identifying an appropriate place to initiate handover based on the computed coverage area of cellular base station or wireless local area network (WLAN) access point. Then, inappropriate networks are eliminated for determining the better network to perform handover. Accordingly, a network selection approach is introduced on the basis ofthe Fractional-dolphin echolocation-based support vector neural network (Fractional-DE-based SVNN). The Fractional-DE is designed by integrating Fractional calculus (FC) in Dolphin echolocation (DE), and thereby, modifying the update rule of the DE algorithm based on the location of the solutions in past iterations. The proposed Fractional-DE algorithm is used to train Support vector neural network (SVNN) for selecting the best weights. Several parameters, like Bit error rate (BER), End to end delay (EED), jitter, packet loss, and energy consumption are considered for choosing the best network.

The performance of the proposed VHO mechanism based on Fractional-DE is evaluated based on delay, energy consumption, staytime, and throughput. The proposed Fractional-DE method achieves the minimal delay of 0.0100 sec, the minimal energy consumption of 0.348, maximal staytime of 4.373 sec, and the maximal throughput of 109.20 kbps.

In this paper, a network selection approach is introduced on the basis of the Fractional-Dolphin Echolocation-based Support vector neural network (Fractional-DE-based SVNN). The Fractional-DE is designed by integrating Fractional calculus (FC) in Dolphin echolocation (DE), and thereby, modifying the update rule of the DE algorithm based on the location of the solutions in past iterations. The proposed Fractional-DE algorithm is used to train SVNN for selecting the best weights. Several parameters, like Bit error rate (BER), End to end delay (EED), jitter, packet loss, and energy consumption are considered for choosing the best network.The performance of the proposed VHO mechanism based on Fractional-DE is evaluated based on delay, energy consumption, staytime, and throughput, in which the proposed method offers the best performance.

The purpose of this paper is to present a detailed performance comparison of recent and state-of-the-art population-based optimization algorithms for the air traffic control problem.

Landing sequence and corresponding landing times for the aircrafts were determined by using population-based optimization algorithms such as artificial bee colony, particle swarm, differential evolution, biogeography-based optimization, simulated annealing, firefly and teaching–learning-based optimization. To obtain a fair comparison, all simulations were repeated 30 times for each of the seven algorithms, two different problems and two different population sizes, and many different criteria were used.

Compared to conventional methods that depend on a single solution at the same time, population-based algorithms have simultaneously produced many alternate possible solutions that can be used recursively to achieve better results.

In some cases, it may take slightly longer to obtain the optimum landing sequence and times compared to the methods that give a direct result; however, the processing times can be reduced using powerful computers or GPU computations.

The simulation results showed that using population-based optimization algorithms were useful to obtain optimal landing sequence and corresponding landing times. Thus, the proposed air traffic control method can also be used effectively in real airport applications.

By using population-based algorithms, air traffic control can be performed more effectively. In this way, there will be more efficient planning of passengers’ travel schedules and efficient airport operations.

The study compares the performances of recent and state-of-the-art optimization algorithms in terms of effective air traffic control and provides a useful approach.

Vertical electrical sounding (VES) and Rayleigh wave exploration are widely used in the exploration of near-surface structure, but both have limitations. This study aims to make full use of the advantages of the two methods, reduce the multiple solutions of single inversion and improve the accuracy of the inversion. Thus, a nonlinear joint inversion method of VES and Rayleigh wave exploration based on improved differential evolution (DE) algorithm was proposed.

Based on the DE algorithm, a new initialization strategy was proposed. Then, taking AK-type with high-velocity interlayer model and HA-type with low-velocity interlayer model near the surface as examples, the inversion results of different methods were compared and analyzed. Then, the proposed method was applied to the field data in Chengde, Hebei Province, China. The stratum structure was accurately depicted and verified by drilling.

The synthetic data and field data results showed that the joint inversion of VES and Rayleigh wave data based on the improved DE algorithm can effectively improve the interpretation accuracy of the single-method inversion and had strong stability and large generalizable ability in near-surface engineering problems.

A joint inversion method of VES and Rayleigh wave data based on improved DE algorithm is proposed, which can improve the accuracy of single-method inversion.