Bio-inspired computation: Recent development on the modifications of the cuckoo search algorithm

Combined Economic Emission Dispatch with and without Consideration of PV and Wind Energy by Using Various Optimization Techniques: A Review

Combined economic emission dispatch (CEED) problems are among the most crucial problems in electrical power systems. The purpose of the CEED is to plan the outputs of all production units available in the electrical power system in such a way that the cost of fuel and polluted emissions are minimized while respecting the equality and inequality constraints of the system and efficiently responding to the power load required. The rapid depletion of these sources causes limitation and increases the price of fuel. It is therefore very important that scientific research in the last few decades has been oriented toward the integration of renewable energy systems (RES) such as wind and PV as an alternative source. Furthermore, the CEED problem including RES is the most important problem with regard to electrical power field optimization. In this study, a classification of optimization techniques that are widely used, such as traditional methods, non-conventional methods, and hybrid methods, is summarized. Many optimization methods have been presented and each of them has its own advantages and disadvantages for solving this complex CEED problem, including renewable energy. A review of different optimization techniques for solving this CEED problem is explored in this present paper. This review will encourage researchers in the future to gain knowledge of the best approaches applicable to solve CEED problems for practical electrical systems.

Performance Analysis of Three Side Roughened Solar Air Heater: A Preliminary Investigation

In recent years, sunlight has been used in several fields such as photovoltaic cells, flat plate collectors, solar cookers, green buildings, and agricultural applications. Improved thermal performance has been seen which comes of three sides absorber plate with glass cover compared to the traditional one. This paper presents the Nusselt (Nu) number, collector efficiency factor (CEF), and collector heat removal factor (CHRF) for the optimal solution of three sides artificially roughened solar air heater. Five input variables such as Reynolds (Re) number, relative roughness pitch, relative roughness height, mass flow rate, and air temperature of the duct are taken into account for improved efficiency optimization of collector, collector heat removal factor, and Nu number. Technique for order of preference by similarity to ideal solution (TOPSIS) technique is used to identify the best alternative amongst a number of performance measures by converting them into an equivalent single variable. Moreover, the results revealed the high accuracy of the CEF, CHRF, and Nu number of 75-80%, 74-78%, and 63-71%, respectively. Meanwhile, it has been also observed that roughness Re number varies between 12,500 and 13,500, and height of relative roughness is 0.0245, including pitch of relative roughness 10 along with the rate of mass flow is 0.041 kg/s.

A Lévy Distribution Based Searching Scheme for the Discrete Targets in Vast Region

This paper investigates the Discrete Targets Search Problem, (DTSP), which aims to quickly search for discrete objects scattered in a vast symmetry region. Different from continuous function extremal value search, the discrete points search cannot make use of the properties of regular functions, such as function analytic, single/multiple extreme, and monotonicity. Thus, in this paper a new search scheme based on Lévy random distribution is investigated. In comparison with the TraditionalCarpet search or Random search based on other distributions, DTSP can provide much faster search speed which is demonstrated by simulation with different scales problems for the selected scenarios. The simulations experiment proves that DTSP is faster for searching for a discrete single target or multiple targets in a wide area. It provides a new method for solving the discrete target search problem.

Health state prognostic of fuel cell based on wavelet neural network and cuckoo search algorithm

This paper proposes a novel degradation prognosis of Proton Exchange Membrane Fuel Cell (PEMFC) based on Wavelet Neural Network (WNN) and Cuckoo Search Algorithm (CSA). The proposed method considering the main operating conditions of PEMFC can be applied to the health state prognostic of PEMFC under different conditions. First, the operating data of PEMFC are reconstructed by the locally weighted scatterplot smoothing method to filter noise. Then, the WNN that can analyze the degradation characteristics of PEMFC (global degradation trend and reversible phenomena) is adopted to build the degradation model of PEMFC. Finally, the structure and parameters of WNN are optimized by CSA to improve the accuracy for the degradation prognosis of PEMFC. The optimized degradation prognosis method is used to predict the remaining useful life of PEMFC. The proposed prognostic method is validated by 3 degradation tests of PEMFC under different conditions. The results show that CSA can greatly improve the degradation prognosis accuracy of PEMFC based on WNN. The proposed CSA-WNN can achieve higher precision than other traditional prognostic methods.

Dragonfly Algorithm and Its Applications in Applied Science Survey

One of the most recently developed heuristic optimization algorithms is dragonfly by Mirjalili. Dragonfly algorithm has shown its ability to optimizing different real-world problems. It has three variants. In this work, an overview of the algorithm and its variants is presented. Moreover, the hybridization versions of the algorithm are discussed. Furthermore, the results of the applications that utilized the dragonfly algorithm in applied science are offered in the following area: machine learning, image processing, wireless, and networking. It is then compared with some other metaheuristic algorithms. In addition, the algorithm is tested on the CEC-C06 2019 benchmark functions. The results prove that the algorithm has great exploration ability and its convergence rate is better than the other algorithms in the literature, such as PSO and GA. In general, in this survey, the strong and weak points of the algorithm are discussed. Furthermore, some future works that will help in improving the algorithm's weak points are recommended. This study is conducted with the hope of offering beneficial information about dragonfly algorithm to the researchers who want to study the algorithm.

Multi-Leader Comprehensive Learning Particle Swarm Optimization with Adaptive Mutation for Economic Load Dispatch Problems

Particle swarm optimization (PSO) is one of the most popular, nature inspired optimization algorithms. The canonical PSO is easy to implement and converges fast, however, it suffers from premature convergence. The comprehensive learning particle swarm optimization (CLPSO) can achieve high exploration while it converges relatively slowly on unimodal problems. To enhance the exploitation of CLPSO without significantly impairing its exploration, a multi-leader (ML) strategy is combined with CLPSO. In ML strategy, a group of top ranked particles act as the leaders to guide the motion of the whole swarm. Each particle is randomly assigned with an individual leader and the leader is refreshed dynamically during the optimization process. To activate the stagnated particles, an adaptive mutation (AM) strategy is introduced. Combining the ML and the AM strategies with CLPSO simultaneously, the resultant algorithm is referred to as multi-leader comprehensive learning particle swarm optimization with adaptive mutation (ML-CLPSO-AM). To evaluate the performance of ML-CLPSO-AM, the CEC2017 test suite was employed. The test results indicate that ML-CLPSO-AM performs better than ten popular PSO variants and six other types of representative evolutionary algorithms and meta-heuristics. To validate the effectiveness of ML-CLPSO-AM in real-life applications, ML-CLPSO-AM was applied to economic load dispatch (ELD) problems.

A Hybrid Ant Colony and Cuckoo Search Algorithm for Route Optimization of Heating Engineering

The development of remote sensing and intelligent algorithms create an opportunity to include ad hoc technology in the heating route design area. In this paper, classification maps and heating route planning regulations are introduced to create the fitness function. Modifications of ant colony optimization and the cuckoo search algorithm, as well as a hybridization of the two algorithms, are proposed to solve the specific Zhuozhou–Fangshan heating route design. Compared to the fitness function value of the manual route (234.300), the best route selected by modified ant colony optimization (ACO) was 232.343, and the elapsed time for one solution was approximately 1.93 ms. Meanwhile, the best route selected by modified Cuckoo Search (CS) was 244.247, and the elapsed time for one solution was approximately 0.794 ms. The modified ant colony optimization algorithm can find the route with smaller fitness function value, while the modified cuckoo search algorithm can find the route overlapped to the manual selected route better. The modified cuckoo search algorithm runs more quickly but easily sticks into the premature convergence. Additionally, the best route selected by the hybrid ant colony and cuckoo search algorithm is the same as the modified ant colony optimization algorithm (232.343), but with higher efficiency and better stability.