Even Search in a Promising Region for Constrained Multi-Objective Optimization

In recent years, a large number of approaches to constrained multi-objective optimization problems(CMOPs) have been proposed, focusing on developing tweaked strategies and techniques for handling constraints. However, an overly finetuned strategy or technique might overfit some problem types,resulting in a lack of versatility. In this article, we propose a generic search strategy that performs an even search in a promising region. The promising region, determined by obtained feasible non-dominated solutions, possesses two general properties.First, the constrained Pareto front(CPF) is included in the promising region. Second, as the number of feasible solutions increases or the convergence performance(i.e., approximation to the CPF) of these solutions improves, the promising region shrinks. Then we develop a new strategy named even search,which utilizes the non-dominated solutions to accelerate convergence and escape from local optima, and the feasible solutions under a constraint relaxation condition to exploit and detect feasible regions. Finally, a diversity measure is adopted to make sure that the individuals in the population evenly cover the valuable areas in the promising region. Experimental results on 45 instances from four benchmark test suites and 14 real-world CMOPs have demonstrated that searching evenly in the promising region can achieve competitive performance and excellent versatility compared to 11 most state-of-the-art methods tailored for CMOPs.

Application of a Parallel Adaptive Cuckoo Search Algorithm in the Rectangle Layout Problem

The meta-heuristic algorithm is a global probabilistic search algorithm for the iterative solution.It has good performance in global optimization fields such as maximization.In this paper,a new adaptive parameter strategy and a parallel communication strategy are proposed to further improve the Cuckoo Search(CS)algorithm.This strategy greatly improves the convergence speed and accuracy of the algorithm and strengthens the algorithm’s ability to jump out of the local optimal.This paper compares the optimization performance of Parallel Adaptive Cuckoo Search(PACS)with CS,Parallel Cuckoo Search(PCS),Particle Swarm Optimization(PSO),Sine Cosine Algorithm(SCA),Grey Wolf Optimizer(GWO),Whale Optimization Algorithm(WOA),Differential Evolution(DE)and Artificial Bee Colony(ABC)algorithms by using the CEC-2013 test function.The results show that PACS algorithmoutperforms other algorithms in 20 of 28 test functions.Due to the superior performance of PACS algorithm,this paper uses it to solve the problem of the rectangular layout.Experimental results show that this scheme has a significant effect,and the material utilization rate is improved from89.5%to 97.8%after optimization.

Adaptive allocation strategy for cooperatively jamming netted radar system based on improved cuckoo search algorithm

The jamming resource allocation problem of the aircraft formation cooperatively jamming netted radar system is investigated.An adaptive allocation strategy based on dynamic adaptive discrete cuckoo search algorithm(DADCS)is proposed,whose core is to adjust allocation scheme of limited jamming resource of aircraft formation in real time to maintain the best jamming effectiveness against netted radar system.Firstly,considering the information fusion rules and different working modes of the netted radar system,a two-factor jamming effectiveness evaluation function is constructed,detection probability and aiming probability are adopted to characterize jamming effectiveness against netted radar system in searching and tracking mode,respectively.Then a nonconvex optimization model for cooperatively jamming netted radar system is established.Finally,a dynamic adaptive discrete cuckoo search algorithm(DADCS)is constructed by improving path update strategies and introducing a global learning mechanism,and a three-step solution method is proposed subsequently.Simulation results are provided to demonstrate the advantages of the proposed optimization strategy and the effectiveness of the improved algorithm.

Cuckoo search algorithm-based optimal deployment method of heterogeneous multistatic radar for barrier coverage

This paper proposes an optimal deployment method of heterogeneous multistatic radars to construct arc barrier coverage with location restrictions.This method analyzes and proves the properties of different deployment patterns in the optimal deployment sequence.Based on these properties and considering location restrictions,it introduces an optimization model of arc barrier coverage and aims to minimize the total deployment cost of heterogeneous multistatic radars.To overcome the non-convexity of the model and the non-analytical nature of the objective function,an algorithm combining integer line programming and the cuckoo search algorithm(CSA)is proposed.The proposed algorithm can determine the number of receivers and transmitters in each optimal deployment squence to minimize the total placement cost.Simulations are conducted in different conditions to verify the effectiveness of the proposed method.

Ensuring Information Security in Electronic Health Record System Using Cryptography and Cuckoo Search Algorithm

In the contemporary era,the abundant availability of health information through internet and mobile technology raises concerns.Safeguarding and maintaining the confidentiality of patients’medical data becomes paramount when sharing such information with authorized healthcare providers.Although electronic patient records and the internet have facilitated the exchange of medical information among healthcare providers,concerns persist regarding the security of the data.The security of Electronic Health Record Systems(EHRS)can be improved by employing the Cuckoo Search Algorithm(CS),the SHA-256 algorithm,and the Elliptic Curve Cryptography(ECC),as proposed in this study.The suggested approach involves usingCS to generate the ECCprivate key,thereby enhancing the security of data storage in EHR.The study evaluates the proposed design by comparing encoding and decoding times with alternative techniques like ECC-GA-SHA-256.The research findings indicate that the proposed design achieves faster encoding and decoding times,completing 125 and 175 iterations,respectively.Furthermore,the proposed design surpasses other encoding techniques by exhibiting encoding and decoding times that are more than 15.17%faster.These results imply that the proposed design can significantly enhance the security and performance of EHRs.Through the utilization of CS,SHA-256,and ECC,this study presents promising methods for addressing the security challenges associated with EHRs.

Quantum walk search algorithm for multi-objective searching with iteration auto-controlling on hypercube

Shenvi et al.have proposed a quantum algorithm based on quantum walking called Shenvi-Kempe-Whaley(SKW)algorithm,but this search algorithm can only search one target state and use a specific search target state vector.Therefore,when there are more than two target nodes in the search space,the algorithm has certain limitations.Even though a multiobjective SKW search algorithm was proposed later,when the number of target nodes is more than two,the SKW search algorithm cannot be mapped to the same quotient graph.In addition,the calculation of the optimal target state depends on the number of target states m.In previous studies,quantum computing and testing algorithms were used to solve this problem.But these solutions require more Oracle calls and cannot get a high accuracy rate.Therefore,to solve the above problems,we improve the multi-target quantum walk search algorithm,and construct a controllable quantum walk search algorithm under the condition of unknown number of target states.By dividing the Hilbert space into multiple subspaces,the accuracy of the search algorithm is improved from p_(c)=(1/2)-O(1/n)to p_(c)=1-O(1/n).And by adding detection gate phase,the algorithm can stop when the amplitude of the target state becomes the maximum for the first time,and the algorithm can always maintain the optimal number of iterations,so as to reduce the number of unnecessary iterations in the algorithm process and make the number of iterations reach t_(f)=(π/2)(?).

Do Search and Selection Operators Play Important Roles in Multi-Objective Evolutionary Algorithms:A Case Study

Multi-objective Evolutionary Algorithm (MOEA) is becoming a hot research area and quite a few aspects of MOEAs have been studied and discussed. However there are still few literatures discussing the roles of search and selection operators in MOEAs. This paper studied their roles by solving a case of discrete Multi-objective Optimization Problem (MOP): Multi-objective TSP with a new MOEA. In the new MOEA, We adopt an efficient search operator, which has the properties of both crossover and mutation, to generate the new individuals and chose two selection operators: Family Competition and Population Competition with probabilities to realize selection. The simulation experiments showed that this new MOEA could get good uniform solutions representing the Pareto Front and outperformed SPEA in almost every simulation run on this problem. Furthermore, we analyzed its convergence property using finite Markov chain and proved that it could converge to Pareto Front with probability 1. We also find that the convergence property of MOEAs has much relationship with search and selection operators.

Research on the MPPT of Photovoltaic Power Generation Based on the CSA-INC Algorithm

The existing Maximum Power Point Tracking(MPPT)method has low tracking efficiency and poor stability.It is easy to fall into the Local Maximum Power Point(LMPP)in Partial Shading Condition(PSC),resulting in the degradation of output power quality and efficiency.It was found that various bio-inspired MPPT based optimization algorithms employ different mechanisms,and their performance in tracking the Global Maximum Power Point(GMPP)varies.Thus,a Cuckoo search algorithm(CSA)combined with the Incremental conductance Algorithm(INC)is proposed(CSA-INC)is put forward for the MPPT method of photovoltaic power generation.The method can improve the tracking speed by more than 52%compared with the traditional Cuckoo Search Algorithm(CSA),and the results of the study using this algorithm are compared with the popular Particle Swarm Optimization(PSO)and the Gravitational Search Algorithm(GSA).CSA-INC has an average tracking efficiency of 99.99%and an average tracking time of 0.19 s when tracking the GMPP,which improves PV power generation’s efficiency and power quality.

Parameter estimation for chaotic systems using the cuckoo search algorithm with an orthogonal learning method

We study the parameter estimation of a nonlinear chaotic system,which can be essentially formulated as a multidimensional optimization problem.In this paper,an orthogonal learning cuckoo search algorithm is used to estimate the parameters of chaotic systems.This algorithm can combine the stochastic exploration of the cuckoo search and the exploitation capability of the orthogonal learning strategy.Experiments are conducted on the Lorenz system and the Chen system.The proposed algorithm is used to estimate the parameters for these two systems.Simulation results and comparisons demonstrate that the proposed algorithm is better or at least comparable to the particle swarm optimization and the genetic algorithm when considering the quality of the solutions obtained.

Structural reliability analysis using enhanced cuckoo search algorithm and artificial neural network

The present study proposed an enhanced cuckoo search(ECS) algorithm combined with artificial neural network(ANN) as the surrogate model to solve structural reliability problems. In order to enhance the accuracy and convergence rate of the original cuckoo search(CS) algorithm, the main parameters namely, abandon probability of worst nests paand search step sizeα0 are dynamically adjusted via nonlinear control equations. In addition, a global-best guided equation incorporating the information of global best nest is introduced to the ECS to enhance its exploitation. Then, the proposed ECS is linked to the well-trained ANN model for structural reliability analysis. The computational capability of the proposed algorithm is validated using five typical structural reliability problems and an engineering application. The comparison results show the efficiency and accuracy of the proposed algorithm.

Wind speed forecasting based on wavelet decomposition and wavelet neural networks optimized by the Cuckoo search algorithm

Wind speed forecasting is of great importance for wind farm management and plays an important role in grid integration. Wind speed is volatile in nature and therefore it is difficult to predict with a single model. In this study, three hybrid multi-step wind speed forecasting models are developed and compared — with each other and with earlier proposed wind speed forecasting models. The three models are based on wavelet decomposition(WD), the Cuckoo search(CS) optimization algorithm, and a wavelet neural network(WNN). They are referred to as CS-WD-ANN(artificial neural network), CS-WNN, and CS-WD-WNN, respectively. Wind speed data from two wind farms located in Shandong, eastern China, are used in this study. The simulation result indicates that CS-WD-WNN outperforms the other two models, with minimum statistical errors. Comparison with earlier models shows that CS-WD-WNN still performs best, with the smallest statistical errors. The employment of the CS optimization algorithm in the models shows improvement compared with the earlier models.

A hybrid cuckoo search algorithm with feasibility-based rule for constrained structural optimization

Constrained optimization problems are very important as they are encountered in many science and engineering applications.As a novel evolutionary computation technique,cuckoo search(CS) algorithm has attracted much attention and wide applications,owing to its easy implementation and quick convergence.A hybrid cuckoo pattern search algorithm(HCPS) with feasibility-based rule is proposed for solving constrained numerical and engineering design optimization problems.This algorithm can combine the stochastic exploration of the cuckoo search algorithm and the exploitation capability of the pattern search method.Simulation and comparisons based on several well-known benchmark test functions and structural design optimization problems demonstrate the effectiveness,efficiency and robustness of the proposed HCPS algorithm.

Solving material distribution routing problem in mixed manufacturing systems with a hybrid multi-objective evolutionary algorithm

The material distribution routing problem in the manufacturing system is a complex combinatorial optimization problem and its main task is to deliver materials to the working stations with low cost and high efficiency. A multi-objective model was presented for the material distribution routing problem in mixed manufacturing systems, and it was solved by a hybrid multi-objective evolutionary algorithm (HMOEA). The characteristics of the HMOEA are as follows: 1) A route pool is employed to preserve the best routes for the population initiation; 2) A specialized best?worst route crossover (BWRC) mode is designed to perform the crossover operators for selecting the best route from Chromosomes 1 to exchange with the worst one in Chromosomes 2, so that the better genes are inherited to the offspring; 3) A route swap mode is used to perform the mutation for improving the convergence speed and preserving the better gene; 4) Local heuristics search methods are applied in this algorithm. Computational study of a practical case shows that the proposed algorithm can decrease the total travel distance by 51.66%, enhance the average vehicle load rate by 37.85%, cut down 15 routes and reduce a deliver vehicle. The convergence speed of HMOEA is faster than that of famous NSGA-II.

A Hybrid Multi-Objective Evolutionary Algorithm for Optimal Groundwater Management under Variable Density Conditions

In this paper, a new hybrid multi-objective evolutionary algorithm （MOEA）, the niched Pareto tabu search combined with a genetic algorithm （NPTSGA）, is proposed for the management of groundwater resources under variable density conditions. Relatively few MOEAs can possess global search ability contenting with intensified search in a local area. Moreover, the overall searching ability of tabu search （TS） based MOEAs is very sensitive to the neighborhood step size. The NPTSGA is developed on the thought of integrating the genetic algorithm （GA） with a TS based MOEA, the niched Pareto tabu search （NPTS）, which helps to alleviate both of the above difficulties. Here, the global search ability of the NPTS is improved by the diversification of candidate solutions arising from the evolving genetic algorithm population. Furthermore, the proposed methodology coupled with a density-dependent groundwater flow and solute transport simulator, SEAWAT, is developed and its performance is evaluated through a synthetic seawater intrusion management problem. Optimization results indicate that the NPTSGA offers a tradeoff between the two conflicting objectives. A key conclusion of this study is that the NPTSGA keeps the balance between the intensification of nondomination and the diversification of near Pareto-optimal solutions along the tradeoff curves and is a stable and robust method for implementing the multi-objective design of variable-density groundwater resources.

Control allocation for aircraft with input constraints based on improved cuckoo search algorithm

The control allocation problem of aircraft whose control inputs contain integer constraints is investigated. The control allocation problem is described as an integer programming problem and solved by the cuckoo search algorithm. In order to enhance the search capability of the cuckoo search algorithm, the adaptive detection probability and amplification factor are designed. Finally, the control allocation method based on the proposed improved cuckoo search algorithm is applied to the tracking control problem of the innovative control effector aircraft. The comparative simulation results demonstrate the superiority and effectiveness of the proposed improved cuckoo search algorithm in control allocation of aircraft.

A Method for Crude Oil Selection and Blending Optimization Based on Improved Cuckoo Search Algorithm

Refineries often need to find similar crude oil to replace the scarce crude oil for stabilizing the feedstock property. We introduced the method for calculation of crude blended properties firstly, and then created a crude oil selection and blending optimization model based on the data of crude oil property. The model is a mixed-integer nonlinear programming(MINLP) with constraints, and the target is to maximize the similarity between the blended crude oil and the objective crude oil. Furthermore, the model takes into account the selection of crude oils and their blending ratios simultaneously, and transforms the problem of looking for similar crude oil into the crude oil selection and blending optimization problem. We applied the Improved Cuckoo Search(ICS) algorithm to solving the model. Through the simulations, ICS was compared with the genetic algorithm, the particle swarm optimization algorithm and the CPLEX solver. The results show that ICS has very good optimization efficiency. The blending solution can provide a reference for refineries to find the similar crude oil. And the method proposed can also give some references to selection and blending optimization of other materials.

Enhanced Energy Efficient Multipath Routing Protocol for Wireless Sensor Communication Networks Using Cuckoo Search Algorithm

Energy efficient routing is one of the major thrust areas in Wireless Sensor Communication Networks (WSCNs) and it attracts most of the researchers by its valuable applications and various challenges. Wireless sensor networks contain several nodes in its terrain region. Reducing the energy consumption over the WSCN has its significance since the nodes are battery powered. Various research methodologies were proposed by researchers in this area. One of the bio-inspired computing paradigms named Cuckoo search algorithm is used in this research work for finding the energy efficient path and routing is performed. Several performance metrics are taken into account for determining the performance of the proposed routing protocol such as throughput, packet delivery ratio, energy consumption and delay. Simulation is performed using NS2 and the results shows that the proposed routing protocol is better in terms of average throughput, and average energy consumption.

A Novel Cuckoo Search Algorithm and Its Application

In this paper, the principle of Cuckoo algorithm is introduced, and the traditional Cuckoo algorithm is improved to establish a mathematical model of multi-objective optimization scheduling. Based on the improved algorithm, the model is optimized to a certain extent. Through analysis, it is proved that the improved algorithm has higher computational accuracy and can effectively improve the global convergence.

A Novel Method for Identifying Recursive Systematic Convolutional Encoders Based on the Cuckoo Search Algorithm

The existing methods for identifying recursive systematic convolutional encoders with high robustness require to test all the candidate generator matrixes in the search space exhaustively.With the increase of the codeword length and constraint length,the search space expands exponentially,and thus it limits the application of these methods in practice.To overcome the limitation,a novel identification method,which gets rid of exhaustive test,is proposed based on the cuckoo search algorithm by using soft-decision data.Firstly,by using soft-decision data,the probability that a parity check equation holds is derived.Thus,solving the parity check equations is converted to maximize the joint probability that parity check equations hold.Secondly,based on the standard cuckoo search algorithm,the established cost function is optimized.According to the final solution of the optimization problem,the generator matrix of recursive systematic convolutional code is estimated.Compared with the existing methods,our proposed method does not need to search for the generator matrix exhaustively and has high robustness.Additionally,it does not require the prior knowledge of the constraint length and is applicable in any modulation type.

A Cuckoo Search Detector Generation-based Negative Selection Algorithm

The negative selection algorithm(NSA)is an adaptive technique inspired by how the biological immune system discriminates the self from nonself.It asserts itself as one of the most important algorithms of the artificial immune system.A key element of the NSA is its great dependency on the random detectors in monitoring for any abnormalities.However,these detectors have limited performance.Redundant detectors are generated,leading to difficulties for detectors to effectively occupy the non-self space.To alleviate this problem,we propose the nature-inspired metaheuristic cuckoo search(CS),a stochastic global search algorithm,which improves the random generation of detectors in the NSA.Inbuilt characteristics such as mutation,crossover,and selection operators make the CS attain global convergence.With the use of Lévy flight and a distance measure,efficient detectors are produced.Experimental results show that integrating CS into the negative selection algorithm elevated the detection performance of the NSA,with an average increase of 3.52%detection rate on the tested datasets.The proposed method shows superiority over other models,and detection rates of 98%and 99.29%on Fisher’s IRIS and Breast Cancer datasets,respectively.Thus,the generation of highest detection rates and lowest false alarm rates can be achieved.