Improved genetic algorithm for mixed-discrete-continuous design optimization problems

The most important feature of radar on missile is target tracking. However, due to the radar's type of belt drive, there is a vibration resistance problem that brings too large radar system error in vibration condition between arc tooth structure. The purpose of this paper is to achieve the reduction of radar system error by using intelligent algorithms to adjust parameters of radar control system and looking for the optimum combination of parameters. In this study, based on the existing missile's radar control system model, we set up six performance indicators to evaluate radar system error. Therafter, the traditional Genetic Algorithm(GA), the Improved Genetic Algorithms(IGA), the Particle Swarm Optimization(PSO), the Particle Swarm Optimization — Improved Genetic Algorithm (PSO-IGA) were used for making the performance function minimum. The results show that it can effectively reduce radar system error by adjusting the parameters, and PSO-IGA overwhelms the others in terms of the quality of solutions and convergence rate.

Optimisation of fixture layout is critical to reduce geometric and form error of the workpiece during the machining process. In this paper the optimal placement of fixture elements (locator and clamp locations) under dynamic conditions is investigated using evolutionary techniques. The application of the newly developed particle swarm optimisation (PSO) algorithm and widely used genetic algorithm (GA) is presented to minimise elastic deformation of the workpiece considering its dynamic response. To improve the performances of GA and PSO, an improved GA (IGA) obtained by basic GA (GA) with sharing and adaptive mutation and an improved PSO (IPSO) obtained by basic PSO (PSO) incorporated into adaptive mutation are developed. ANSYS parametric design language (APDL) of finite element analysis is employed to compute the objective function for a given fixture layout. Three layout optimisation cases derived from the high speed slot milling case are used to test the effectiveness of the GA, IGA, PSO and IPSO based approaches. The comparisons of computational results show that IPSO seems superior to GA, IGA and PSO approaches with respect to the trade-off between global optimisation capability and convergence speed for the presented type problems.

This paper proposed an uncertain delivery time control model for Fourth Party Logistics (4PL). The selection of Third Party Logistics (3PL) suppliers and transportation routes, delivery time and transportation cost are included. An Improved Genetic Algorithm (I-GA) is designed to solve the resulting optimization problem. In detail, the reverse-two-point method is applied in crossover operation. In the experiment, Enumeration, Genetic Algorithm(GA) and Tabu Search Genetic Algorithm hybrid algorithm (TS-GA) are also used to compare with I-GA. The simulation results and analysis shows that the proposed model and algorithm is very useful for supporting the decision on the process of 4PL operation.

The implementation of automatically fitting the theoretical model of spatial variogram has always been a problem in the realm of geostatistics. Considering that the genetic algorithm has sufficient robustness and it has the virtue of searching the global optimum while solving the nonlinear problems, so this paper takes the method of genetic algorithm to automatically fit the theoretical model of spatial variogram. However, the traditional genetic algorithm has the deficiency of slow convergence rate and gets premature convergence easily, according to which this paper proposes a kind of new improved genetic algorithm: comprehensively considering the optimal preservation, self-adaptive regulated probability of intercross and of mutation, introducing the simulated annealing algorithm to revise the fitness function and the survival mechanism. The validation of typical cases and application in fitting the spatial variogram show that this improved algorithm converges quickly and is globally convergent. It is worthy to say that the improved genetic algorithm is a simple and effective method of fitting spatial variogram and other nonlinear problems.

Genetic association is a challenging task for the identification and characterization of genes that increase the susceptibility to common complex multifactorial diseases. To fully execute genetic studies of complex diseases, modern geneticists face the challenge of detecting interactions between loci. A genetic algorithm (GA) is developed to detect the association of genotype frequencies of cancer cases and noncancer cases based on statistical analysis. An improved genetic algorithm (IGA) is proposed to improve the reliability of the GA method for high-dimensional SNP-SNP interactions. The strategy offers the top five results to the random population process, in which they guide the GA toward a significant search course. The IGA increases the likelihood of quickly detecting the maximum ratio difference between cancer cases and noncancer cases. The study systematically evaluates the joint effect of 23 SNP combinations of six steroid hormone metabolisms, and signaling-related genes involved in breast carcinogenesis pathways were systematically evaluated, with IGA successfully detecting significant ratio differences between breast cancer cases and noncancer cases. The possible breast cancer risks were subsequently analyzed by odds-ratio (OR) and risk-ratio analysis. The estimated OR of the best SNP barcode is significantly higher than 1 (between 1.15 and 7.01) for specific combinations of two to 13 SNPs. Analysis results support that the IGA provides higher ratio difference values than the GA between breast cancer cases and noncancer cases over 3-SNP to 13-SNP interactions. A more specific SNP-SNP interaction profile for the risk of breast cancer is also provided.

Tracking control of induction motor using fuzzy phase plane controller with improved genetic algorithm

In order to attain global optimal or quasioptimum solution for power transformers design, some interrelated key techniques such as encoding scheme, genetic operators, constrained condition, fitness function for the simple genetic algorithm (SGA) are further reformed and researched. An improved genetic algorithm (IGA) is developed in this paper and applied to the optimum design of S9 power transformers for the first time. In addition, a multi-objective algorithm based on IGA is applied successfully in the double objective optimum design of S9 power transformers, by using the theory of variable weight coefficients for the multi-objective optimization. All the achievements in the paper are verified by a representative mathematical example and a practical S9-1000/10 kV power transformer. All the optimization results are satisfactory and show that IGA has powerful ability of global searching, excellent solution precision and has a bright application prospect in the fields of power transformers design.

The power consumption of reefers in container yards is affected by the number of reefers, the type of cargo and the working state (refrigeration or insulation), resulting in significant fluctuations that can impact the stability of power supply facilities. To address this issue, this paper proposes a power balancing optimization model that minimizes peak-to-valley differences in the reefer yard. The model dynamically determines the working state of each reefer to achieve unified scheduling of the cluster of reefers in the yard. To address uncertainties in the process of inbound storage and outbound pick-up of reefers, an agent-based simulation, and an improved genetic algorithm (IGA) optimization method are used. We compare the improvement effect of the IGA and the optimization efficiency of the method under different decision times through a case study. Experimental results reveal that both the basic genetic algorithm (GA) and the IGA can reduce the peak-to-valley difference, but the IGA is faster and more effective. The proposed optimization model and solution approach enable quick and efficient identification of optimal scheduling strategies for the reefers in a container yard, providing valuable guidance for the intelligent scheduling of reefers in container terminals.

The Boolean functions satisfying secure properties on the restricted sets of inputs are studied recently due to their importance in the framework of the FLIP stream cipher. However, finding Boolean functions with optimal cryptographic properties is an open research problem in the cryptographic community. This paper presents an Improved Genetic Algorithm (IGA) with the directed changes that keep the weightwise balancedness of Boolean functions. A cross-protection strategy is proposed to ensure that the offspring has the same weightwise balancedness characteristics of the parents while implementing crossover. Then, a large number of weightwise (almost) perfectly balanced (W(A)PB) functions with a good nonlinearity profile are obtained based on IGA. Finally, we make comparisons between our constructions and relevant works. The comparisons show that IGA has a significant advantage for reaching the W(A)PB functions with high weightwise nonlinearity. Moreover, it is the first time to obtain the 8-variable WPB functions with the weightwise nonlinearity of 28 in the restricted sets of inputs with Hamming weight of 4, and list the statistical indicators of the weightwise nonlinearity for W(A)PB functions for input size n = 9, 10.

This paper proposed a method of improved genetic algorithm (IGA)-based secure communications in multiple time delay chaotic (MTDC) systems with optimal H <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sup> performance and advanced encryption standard (AES). First, we apply an initial round key via AES encryption function to acquire the encrypted message. The encrypted message is used to carry out double encryption by chaotic synchronization. A robust fuzzy control design is then presented to address the effects of modeling errors between the MTDC systems and Takagi-Sugeno (T-S) fuzzy models. Next, in terms of Lyapunov's direct method, a delay dependent exponential stability criterion is derived to guarantee that the trajectories of the slave system can approach those of the master system. Subsequently, the stability conditions of this criterion are transformed into linear matrix inequalities (LMIs). In addition, the genetic algorithm (GA) has drawn great attention from researchers due to the capability of GA in random search for achieving near-optimal solutions. The lower and upper bounds of the search space based on the LMI approach can be set so that the GA will seek better feedback gains of fuzzy controllers in order to speed up the synchronization. However, the GA has the shortcomings of premature convergence and local search. Therefore, an improved genetic algorithm (IGA) is proposed in this study to effectively overcome the weakness of the traditional GA. On the basis of the IGA, a fuzzy controller is synthesized to not only realize the exponential synchronization, but also achieve optimal H <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sup> performance by minimizing the disturbance attenuation level. Finally, a numerical example with simulations is given to demonstrate the effectiveness of our approach.

The travelling salesman problem (TSP) is a benchmark problem in which a salesman has to visit all nodes (cities) in a network exactly once except the starting node, come back to the starting node and find the shortest tour. Genetic algorithm (GA) is one of the best algorithms to deal with the travelling salesman problem (TSP). In GA, crossover operator plays a vital role and the sequential constructive crossover (SCX) is one of the best crossover operators for solving the TSP. Several improvements have been proposed for other crossover operators. In this paper we propose four improved genetic algorithms using three local search methods – 2-opt search, a hybrid mutation, and a combined mutation operator, and incorporate them into SCX. The experimental results on some TSPLIB instances show that our improved GAs can significantly improve simple GA using SCX in terms of solution quality.

Searching secret key of classical ciphers in the keyspace is a challenging NP-complete problem that can be successfully solved using metaheuristic techniques. This article proposes two metaheuristic techniques: improved genetic algorithm (IGA) and a new discrete cuckoo search (CS) algorithm for solving a classical substitution cipher. The efficiency and effectiveness of the proposed techniques are compared to the existing tabu search (TS) and genetic algorithm (GA) techniques using three criteria: (a) average number of key elements correctly detected, (b) average number of keys examined before determining the required key, and (c) the mean performance time. As per the results obtained, the improved GA is comparatively better than the existing GA for criteria (a) and (c), while the proposed CS strategy is significantly better than rest of the algorithms (i.e., GA, IGA, and TS) for all three criteria. The obtained results indicate that the proposed CS technique can be an efficient and effective option for solving other similar NP-complete combinatorial problems also.

Taking an underwater shell structure as engineering background, the Improved Genetic Algorithms (IGAs) is applied to detect the structural damage by static response data acquisition in the paper. Firstly, region water-level and finite point strains of the underwater shell structure are measured. Then, the Genetic Algorithms (GAs) are optimized. Finally, the structural damage location and degree are identified by comparison between measurement strains and calculation ones. When the measured strains are approached most close to the one among all the calculation strains, the structural damage condition is determined. In order to diagnose the large-scale structural damage by GAs, the decoding the damage location and damage index at same time is proposed, as well as, fine-tuning of variables and niche technique are also introduced to improve the convergence efficiency of GAs.

Due to the time-varying and uncertainty of traffic flow, the Intelligent Traffic System (ITS) is applied more and more widely in many cities. Accurate short-term traffic flow forecasting is the core component of the system. An improved Wavelet Neural Network (WNN) prediction model based on the Genetic Algorithm (GA) is put forward in this paper. The Genetic Algorithm proposed according to the law of biological evolution is introduced, and the crossover and mutation probability of the algorithm is improved in this paper. The stretching scale and translation scale of the wavelet function are trained firstly, and then the parameters obtained after the pre-trained are further optimized by adopting the gradient descent method. It overcomes the limitation that the traditional WNN is apt to get the local minimum when optimizing parameters. The simulation results indicate that the WNN model based on the improved Genetic Algorithm (IGA) can forecast the short-term traffic flow more precisely.

The takeaway food is mostly ordered online, and delivered. Some riders may receive orders beyond their capacity, causing resource imbalance. Additionally, uncertain travel time makes it difficult for riders to complete deliveries effectively. Thus, an integer programming is formulated for the online crowdsourced delivery problem with balanced rider resources and uncertain travel time (OCD-BRUT) to optimize rider delivery routes. An improved genetic algorithm (IGA) with order sequence optimization operator is developed. Numerical experiments on both simulated and real-world datasets demonstrate that the OCD-BRUT effectively balances rider resources, especially in medium and large instances. For small to medium instances, the average gap between the IGA and the optimal baseline is −2.58%, while the average gap reaches −7.48% in large-scale instances, indicating IGA’s efficiency in handling numerous orders in rush hours. Besides, a sensitivity analysis of several key parameters is also performed to derive managerial insights.