Hybrid Mutation Operator Research Articles

Retraction Note: Secured and compound 3-D chaos image encryption using hybrid mutation and crossover operator

Retraction Note: Secured and compound 3-D chaos image encryption using hybrid mutation and crossover operator

Multiple strategies based Grey Wolf Optimizer for feature selection in performance evaluation of open-ended funds

The methods for selecting the features in evaluating fund performance rely heavily on traditional statistics, which can potentially lead to excessive data dimensions in a multi-dimensional context. Grey Wolf Optimizer (GWO), a swarm intelligence optimization algorithm with its simple structure and few parameters, is widely used in feature selection. However, the algorithm suffers from local optimality and the imbalance in exploration and exploitation. This paper proposes a Multi-Strategy Grey Wolf Optimizer (MSGWO) to address the limitations, and identify the relevant features for evaluating fund performance. Random Opposition-based Learning is applied to enhance population quality during the initialization phase. Moreover, the convergence factor is nonlinearized to coordinate the global exploration and local exploitation capabilities. Finally, a two-stage hybrid mutation operator is applied to modify the updating mechanism, so as to increase population diversity and balance the exploration and exploitation abilities of GWO. The proposed algorithm is compared against 6 related algorithms and verified by the Wilcoxon signed-rank test on 12 quarterly datasets (2020-2022) of Chinese open-ended funds. The results inform that MSGWO reduces the feature size as well as the classification error rate.

A Pareto-based hybrid genetic simulated annealing algorithm for multi-objective hybrid production line balancing problem considering disassembly and assembly

ABSTRACT Most existing studies about line balancing problems mainly focus on disassembly and assembly separately, which rarely integrate these two modes into a system. However, as critical activities in the remanufacturing field, assembly and disassembly share many similarities, such as working tools and processing sequence. Thus, this paper proposes a multi-objective hybrid production line balancing problem with a fixed number of workstations (HPLBP-FNW) considering disassembly and assembly to optimise cycle time, total cost, and workload smoothness simultaneously. And a novel Pareto-based hybrid genetic simulated annealing algorithm (PB-HGSA) is designed to solve it. In PB-HGSA, the two-point crossover and hybrid mutation operator are proposed to produce potential non-dominated solutions (NDSs). Then, a local search method based on a parallel simulated annealing algorithm is designed for providing a depth search around the NDSs to balance the global and local search ability. Numerical results by comparing PB-HGSA with the well-known algorithms verify the effectiveness of PB-HGSA in solving HPLBP-FNW. Moreover, the managerial insights based on a case study are given to inspire enterprise companies to consider hybrid production line in the remanufacturing process, which is beneficial to reduce the cycle time and total cost and improve the service life of the equipment.

Time-varying external archive differential evolution algorithm with applications to parallel mechanisms

In this work, a time-varying external archive differential evolution algorithm is proposed, which can obtain the multiple roots for system of nonlinear equations for the forward displacement of parallel mechanisms. In time-varying external archive differential evolution three improvements are presented. At first, a time-varying subpopulation strategy is proposed to consider both global and local searching capabilities. Then, an individual judgment criterion is proposed, which employs the weighted basis vector mutation strategy for ordinary individuals and a hybrid mutation operator for locally optimal individuals. Finally, the external archive strategy of neighborhood-based crowding differential evolution is improved to enhance the domain searching ability of the algorithm. To evaluate the performance of time-varying external archive differential evolution, twenty benchmark functions are selected as the test suite. Moreover, the solutions for forward displacements of four strongly coupled parallel mechanisms with different degrees of freedom are derived to test the generality of this method. From the acquired experimental results, it is demonstrated that fairly-competitive results can be provided compared to other methods, in both benchmark functions and forward displacement solutions of parallel mechanisms.

A hybrid optimization algorithm and its application in flight trajectory prediction

In order to improve the optimization performance of differential evolution (DE), a hybrid optimization algorithm (abbreviated as DEHHO) based on DE and Harris hawks optimization (HHO) is proposed. Firstly, the local search operator “HHO/SB” of HHO is combined with and classic mutation operator “DE/RAND” of DE to form a mutation link. Under the influence of the historical evolution state, each individual chooses a more suitable mutation operator to improve the possibility of successful evolution. Secondly, under the control of the historical evolution state, the updating of control parameters at the individual level assists the hybrid mutation operator to balance the population diversity and convergence rate during the evolution process. The performance of DEHHO is verified by a set of universal test benchmarks. On this basis, back propagation neural network (the initial parameters of which are optimized by DEHHO) is used to predict the flight trajectory, which further verifies the performance of DEHHO. Both validation results show that DEHHO outperforms other competitors under the same conditions.

Intelligent Time Allocation for Wireless Power Transfer in Wireless-Powered Mobile Edge Computing

Wireless-powered mobile edge computing is a new network computing paradigm that combines with the advantages of wireless power transfer and mobile edge computing. When the harvest-then-offload protocol is adopted in this network, the time of wireless power transfer has a significant impact on system performance. If the time is too short, the user cannot harvest enough energy. If it is too long, the user will not have enough time to complete the task offloading. Both result in many of user tasks being discarded. To address this problem, DEWPT, a differential evolution-based optimization scheme for wireless power transfer time, is proposed in this paper. DEWPT is designed with a hybrid mutation operator and a perturbation-based binomial crossover operator. The hybrid mutation operator combines the benefits of two mutation operators with distinct characteristics, so that DEWPT not only has a strong exploration ability but also can quickly converge. Meanwhile, the perturbation-based binomial crossover operator improves DEWPT’s ability to exploit local space. These two improvements effectively enhance DEWPT’s optimization performance, which is beneficial to find the optimal time for wireless power transfer. Furthermore, to improve the optimization efficiency, micro-population is introduced into DEWPT. Finally, the computation completion ratio maximization model is used to validate the performance of DEWPT in the wireless-powered mobile edge computing network with multiple edge servers. Numerical results show that the computation offloading scheme integrating with DEWPT can achieve a higher computation completion rate than three benchmark schemes, and is competitive in complexity. This demonstrates that DEWPT is an effective time allocation scheme for wireless power transfer.

A Dual Biogeography-Based Optimization Algorithm for Solving High-Dimensional Global Optimization Problems and Engineering Design Problems

Biogeography-based optimization (BBO) cannot effectively solve high-dimensional global optimization problems due to its single migration mechanism and random mutation operator. To overcome these limitations, this paper propose a dual BBO based on sine cosine algorithm (SCA) and dynamic hybrid mutation, named SCBBO. Firstly, the Latin hypercube sampling method is innovatively used to improve the initial population ergodicity. Secondly, a nonlinear transformation parameter and a inertia weight adjustment factor are designed into the position update formula of SCA to make SCBBO suitable for high dimensional environments. Then, a dynamic hybrid mutation operator is designed by combining Laplacian and Gaussian mutation, which helps the algorithm to escape from local optima and balance the exploration and exploitation. Finally, the dual learning strategy is integrated, so the convergence accuracy is further improved by generating dual individuals. Meanwhile, A sequence convergence model is established to prove the algorithm can converge to the global optimal solution with probability 1. Compared with other state-of-the-art evolutionary algorithms, SCBBO effectively improves the optimization accuracy and convergence speed for high-dimensional optimization problems. To further show the superiority of SCBBO, the performance is compared on 1000, 2000, 5000 and 10000 dimensions, respectively. The comparsions show that SCBBO’s optimization results on these dimensions are basically the same. SCBBO also applied to engineering design problems, and the simulation results demonstrate that the proposed method is also effective on constrained optimization problems.

A hybrid differential evolution based on gaining‑sharing knowledge algorithm and harris hawks optimization

Differential evolution (DE) is favored by scholars for its simplicity and efficiency, but its ability to balance exploration and exploitation needs to be enhanced. In this paper, a hybrid differential evolution with gaining-sharing knowledge algorithm (GSK) and harris hawks optimization (HHO) is proposed, abbreviated as DEGH. Its main contribution lies are as follows. First, a hybrid mutation operator is constructed in DEGH, in which the two-phase strategy of GSK, the classical mutation operator “rand/1” of DE and the soft besiege rule of HHO are used and improved, forming a double-insurance mechanism for the balance between exploration and exploitation. Second, a novel crossover probability self-adaption strategy is proposed to strengthen the internal relation among mutation, crossover and selection of DE. On this basis, the crossover probability and scaling factor jointly affect the evolution of each individual, thus making the proposed algorithm can better adapt to various optimization problems. In addition, DEGH is compared with eight state-of-the-art DE algorithms on 32 benchmark functions. Experimental results show that the proposed DEGH algorithm is significantly superior to the compared algorithms.

Intelligent immune clonal optimization algorithm for pulmonary nodule classification.

Computer-aided diagnosis (CAD) of pulmonary nodules is an effective approach for early detection of lung cancers, and pulmonary nodule classification is one of the key issues in the CAD system. However, CAD has the problems of low accuracy and high false-positive rate (FPR) on pulmonary nodule classification. To solve these problems, a novel method using intelligent immune clonal selection and classification algorithm is proposed and developed in this work. First, according to the mechanism and characteristics of chaotic motion with a logistic mapping, the proposed method utilizes the characteristics of chaotic motion and selects the control factor of the optimal chaotic state, to generate an initial population with randomness and ergodicity. The singleness problem of the initial population of the immune algorithm was solved by the proposed method. Second, considering on the characteristics of Gaussian mutation operator (GMO) with a small scale, and Cauchy mutation operator (CMO) with a big scale, an intelligent mutation strategy is developed, and a novel control factor of the mutation is designed. Therefore, a Gauss-Cauchy hybrid mutation operator is designed. Ultimately, in this study, the intelligent immune clonal optimization algorithm is proposed and developed for pulmonary nodule classification. To verify its accuracy, the proposed method was used to analyze 90 CT scans with 652 nodules. The experimental results revealed that the proposed method had an accuracy of 97.87% and produced 1.52 false positives per scan (FPs/scan), indicating that the proposed method has high accuracy and low FPR.

Improving TSP Solutions Using GA with a New Hybrid Mutation Based on Knowledge and Randomness

Genetic algorithm (GA) is an efficient tool for solving optimization problems by evolving solutions, as it mimics the Darwinian theory of natural evolution. The mutation operator is one of the key success factors in GA, as it is considered the exploration operator of GA.
 Various mutation operators exist to solve hard combinatorial problems such as the TSP. In this paper, we propose a hybrid mutation operator called "IRGIBNNM", this mutation is a combination of two existing mutations; a knowledgebased mutation, and a random-based mutation. We also improve the existing “select best mutation” strategy using the proposed mutation.
 We conducted several experiments on twelve benchmark Symmetric traveling salesman problem (STSP) instances. The results of our experiments show the efficiency of the proposed mutation, particularly when we use it with some other mutations.

A novel case adaptation method based on differential evolution algorithm for disaster emergency

When disasters happen, time is often very urgent. Case-based reasoning (CBR) is one of the most effective approaches to support disaster emergency management. CBR takes good use of historical case data, which is one of the typical data-driven decision-making methods. Among the steps of CBR, adaptation is the core. To improve the adaptation, a hybrid mutation operator is implemented, and a new differential evolution (DE) algorithm is developed. An adaptation method based on the proposed algorithm is put forward to achieve case adaptation in the CBR system. The comparison results have shown that the proposed algorithm is superior compared with the state-of-art algorithms. Then, experiments of CBR have revealed that the adaptation method can effectively generate appropriate solutions with the help of the proposed algorithm.

Intersection Traffic Control Based on Multi-Objective Optimization

Currently, most traffic control methods at intersections rely on the control of signal lights. However, most signal lights operate in the traditional fixed timing mode, which cannot adjust the timing based on the time-varying traffic flow. To solve the problem, this paper constructs a signal timing control model to optimize road capacity, delay time and the number of stops at the intersections, under the following constraints: cycle time, effective green light time and the maximum number of vehicles in each direction of intersection. To solve the model, the standard dragonfly algorithm (DA) was improved by a hybrid mutation operator, which ensures the diversity of solution set. The proposed model and algorithm were compared with the Webster model through simulations in an actual scene and on a virtual platform. The comparison fully proves the advantages of our model and algorithm.

Parameters identification of PMSM based on self-adaptive DE algorithm with hybrid mutation operator

The parameter identification of permanent magnet synchronous motor (PMSM) is an important and challenging task of power electronic systems, which has an important impact on the control performance of the drive system. Due to hardware limitations, this problem requires both a higher solution quality and a faster convergence speed. Aiming at the parameter identification problem, an adaptive differential evolution algorithm based on hybrid mutation operator (SHDE) is proposed. In this method, a randomly selected optimal solution is mixed with the current population, and a new mutation operator is developed, called “current to best archive”. Therefore, the algorithm can use the best search memory to date to generate promising solutions, resulting in a faster evolutionary process. In addition, the SHDO corresponding control parameters are adaptive without the need of trial and error and error processing, and the appropriate control values are obtained. In addition, the parameter estimation program is introduced into the permanent magnet synchronous motor simulation and solved by the Newton-Raphson method without prior assumption and simplification. The framework can be used in any interference-prone working conditions and, unlike other publications, has a wider range of applications. The parameter identification method of permanent magnet synchronous motor drive system under two different operating modes was evaluated. Comprehensive results and statistical analysis show that SHDE can find higher quality solutions with higher convergence speed and probability than other most advanced algorithms.

RETRACTED ARTICLE: Secured and compound 3-D chaos image encryption using hybrid mutation and crossover operator

History, for us, today is data. Events and emotions of the worn centuries have been recorded as data- extremely vulnerable data, which can be easily tampered with. With this being said, it becomes vital for a proper encryption algorithm to assist passage of secure data thorough the web. This paper exclusively concentrates on gray scale images and their subsequent protection by the proposition of a synchronous transformation and substitution system. A Secure and compound 3-D Chaos based image encryption (SCIE) based hybrid methodology based on genetic algorithm has been discussed thoroughly. Here permutation is attained by using arithmetic crossover, multipoint crossovers operator and substitution by combined mutation operators. In this technique 3-D Compound Sine and ICMIC (CSI) map is used to generate key stream. Encryption is done by using hybrid operators with secret key. Unified Average Change Intensity (UACI), correlation coefficient, histogram, Net Pixel Change Rate (NPCR) and entropy were some of the measures which were taken into account to analyse the performance of the recommended algorithm. Upon experimentation of the same, appreciable protection of real- time images has been achieved.

Application of Real-Coded Genetic Algorithm in Ship Weather Routing

Solving the problem of ship weather routing has been always a goal of nautical navigation research and has been investigated by many scientists. The operation schedule of an oceangoing ship can be influenced by wave or wind disturbances, which complicate route planning. In this paper, we present a real-coded genetic algorithm to determine the minimum voyage route time for point-to-point problems in a dynamic environment. A fitness assignment method based on an individual's position in the sorted population is presented, which greatly simplifies the calculation of fitness value. A hybrid mutation operator is proposed to enhance the search for the optimal solution and maintain population diversity. Multi-population techniques and an elite retention strategy are employed to increase population diversity and accelerate convergence rates. The effectiveness of the algorithm is demonstrated by numerical simulation experiments.

Parameter identification of Jiles–Atherton model for magnetostrictive actuator using hybrid niching coral reefs optimization algorithm

The Jiles–Atherton model is widely applied in the description of hysteresis in ferromagnetic, ferroelectric, magnetostrictive and piezoelectric materials, however the parameter identification of it constitutes a challenging problem. In this paper, a hybrid niching coral reefs optimization algorithm (HNCRO) is proposed and implemented for parameter identification of the Jiles–Atherton model for magnetostrictive actuator. To prevent the algorithm from converging to the local optimum, the niche technology based on fitness sharing is introduced into original coral reefs optimization algorithm (OCRO). In order to enhance the local search capability of OCRO, Rosenbrock's rotational direction method is applied to refine the best solution. Compared with OCRO, genetic algorithm, particle swarm optimization, hybrid particle swarm optimization and gravitational search algorithm and differential evolution algorithm with a hybrid mutation operator, the proposed algorithm has better performance in terms of convergence speed, success rate, and accuracy in benchmark functions test. At last, experiments are carried out to verify the effectiveness of the proposed approach on a magnetostrictive actuator. The results demonstrate that the HNCRO is a promising method for parameter identification of the Jiles–Atherton model.

A hybrid particle swarm optimization (PSO) with chi-square and stable mutation jump strategy

Particle Swarm is a heuristic technique based on collective behavior of birds. Several researches depicts that the PSO suffers from untimely convergence. To defeat the issue of untimely convergence in PSO several solutions are proposed to increase the performance in term of accuracy. This paper suggests a new hybrid mutation operator which used Chi-square and stable distribution. The hybrid mutation operator leads the swarm from local minima to global minima for better solution. To validate the new hybrid scheme, a 12 benchmark optimization functions are used in experiment and compared the result with pervious 6 variants of PSO, proposed variant achieved better results than previous 6 variants.

Self-adaptive differential evolution algorithm with hybrid mutation operator for parameters identification of PMSM

Parameters identification of permanent magnetic synchronous motor (PMSM), which significantly influences the control performance of the drive system, is an important and challenging task of power electronic system. The problem requires both high solution quality and fast convergence speed due to the constraints of hardware. This paper presents a self-adaptive differential evolution algorithm with hybrid mutation operator (SHDE) for parameters identification problem. In this method, a novel mutation operator, called “current-to-archive-best,” is developed by mixing the best solutions randomly selected from archive set and current population. Thus, the algorithm could use the best searching memories so far to generate promising solutions, yielding a faster evolving procedure. Besides, the corresponding control parameters of SHDE are also self-adapted without tedious trial-and-error progress to get appropriate values. Moreover, the parameters estimation program is inserted into the PMSM simulation that is solved by using Newton–Raphson method without any pre-assumption and simplification. This framework, which may be used under any working conditions with large disturbance, is different from other publications, resulting in wider applications. The proposed method applied to parameters identification of PMSM is evaluated on a PMSM drive system with two different operations. The comprehensive results and statistical analyses, compared with other state-of-the-art algorithms, show that SHDE could find high-quality solutions with higher convergence speed and probability.

A new differential evolution algorithm with a hybrid mutation operator and self-adapting control parameters for global optimization problems

The differential evolution (DE) algorithm is a notably powerful evolutionary algorithm that has been applied in many areas. Therefore, the question of how to improve the algorithm's performance has attracted considerable attention from researchers. The mutation operator largely impacts the performance of the DE algorithm The control parameters also have a significant influence on the performance. However, it is not an easy task to set a suitable control parameter for DE. One good method is to considering the mutation operator and control parameters simultaneously. Thus, this paper proposes a new DE algorithm with a hybrid mutation operator and self-adapting control parameters. To enhance the searching ability of the DE algorithm, the proposed method categorizes the population into two parts to process different types of mutation operators and self-adapting control parameters embedded in the proposed algorithm framework. Two famous benchmark sets (including 46 functions) are used to evaluate the performance of the proposed algorithm and comparisons with various other DE variants previously reported in the literature have also been conducted. Experimental results and statistical analysis indicate that the proposed algorithm has good performance on these functions.

一种求解约束优化问题基于混合遗传算子的遗传算法

针对约束优化问题的最优解可能位于可行域边界的情况，提出了一种新的基于混合遗传算子的遗传算法。首先，在该算法中，杂交过程按可行个体和不可行个体分别进行，可行个体与最好个体杂交，不可行个体按照约束违反度的大小与可行个体杂交。其次，引入了一个基于边界变异和高斯变异的混合变异算子，其目的是促使不可行解变为可行解，可行解向边界移动。数值实验和比较结果表明了该方法的有效性。 For the case that the optima of constrained optimization problems are often located on boundary of the feasible region, a new genetic algorithm based on hybrid genetic operators is proposed in this paper. First, in this algorithm, the crossover is executed according to feasible and infeasible individuals, respectively. A feasible point is always combined with the best-known one found so far for crossover, whereas an infeasible individual is selected according to the fitness for crossover with any feasible one. In addition, in order to make infeasible solutions become feasible ones and make feasible points move toward the boundary of feasible region, a hybrid mutation operator is presented based on boundary mutation and Gaussian mutation. Numerical experiments and comparison results show the efficiency of the method.