Dual Mutations Collaboration Mechanism with Elites Guiding and Inferiors Eliminating Techniques for Differential Evolution

Differential Evolution (DE) is a powerful evolutionary algorithm for global optimization problems. Generally, appropriate mutation strategies and proper equilibrium between global exploration and local exploitation are significant to the performance of DE. From this consideration, in this paper, we present a novel DE variant, abbreviated to DMIE-DE, to further enhance the optimization capacity of DE by developing a dual mutations collaboration mechanism with elites guiding and inferiors eliminating techniques. More specifically, an explorative mutation strategy DE/current-to-embest with an elite individual serving as part of the difference vector and an exploitative mutation strategy DE/ebest-to-rand with selecting an elite individual as the base vector are employed simultaneously to achieve the balance between local and global performance of the whole population instead of only one mutation strategy in classical DE algorithm. The control parameters F and CR for above mutation strategies are updated adaptively to supplement the optimization ability of DMIE-DE based on a rational probability distribution model and the successful experience from the previous iterations. Moreover, an inferior solutions eliminating technique is embedded to enhance the convergence speed and compensate cost of the fitness evaluation times during the evaluation process. To evaluate the performance of DMIE-DE, experiments are conducted by comparing with five state-of-the-art DE variants on solving 29 test functions in CEC2017 benchmark set. The experimental results indicate that the performance of DMIE-DE is significantly better than, or at least comparable to the considered DE variants.

A hybrid deep kernel incremental extreme learning machine based on improved coyote and beetle swarm optimization methods

The iteration times and learning efficiency of kernel incremental extreme learning machines are always affected by the redundant nodes. A hybrid deep kernel incremental extreme learning machine (DKIELM) based on the improved coyote and beetle swarm optimization methods was proposed in this paper. A hybrid intelligent optimization algorithm based on the improved coyote optimization algorithm (ICOA) and improved beetle swarm optimization algorithm (IBSOA) was proposed to optimize the parameters and determine the number of effectively hidden layer neurons for the proposed DKIELM. A Gaussian global best-growing operator was adopted to replace the original growing operator in the intelligent optimization algorithm to improve COA searching efficiency and convergence. In the meantime, IBSOA was designed based on tent mapping inverse learning and dynamic mutation strategies to avoid falling into a local optimum. The experimental results demonstrated the feasibility and effectiveness of the proposed DKIELM with encouraging performances compared with other ELMs.

Ultrahigh-Dimensional Model and Optimization Algorithm for Resource Allocation in Large-Scale Intelligent D2D Communication System

The optimal resource allocation in the large-scale intelligent device-to-device (D2D) communication system is of great importance for improving system spectrum efficiency and ensuring communication quality. In this study, the D2D resource allocation is modelled as an ultrahigh-dimensional optimization (UHDO) problem with thousands of binary dimensionalities. Then, for efficiently optimizing this UHDO problem, the coupling relationships among those dimensionalities are comprehensively analysed, and several efficient variable-grouping strategies are developed, i.e., cellular user grouping (CU-grouping), D2D pair grouping (DP-grouping), and random grouping (R-grouping). In addition, a novel evolutionary algorithm called the cooperatively coevolving particle swarm optimization with variable-grouping (VGCC-PSO) is developed, in which a novel mutation operation is introduced for ensuring fast satisfaction of constraints. Finally, the proposed UHDO-based allocation model and VGCC-PSO algorithm as well as the grouping and mutation strategies are verified by a comprehensive set of case studies. Simulation results show that the developed VGCC-PSO algorithm performs the best in optimizing the UHDO model with up to 6000 dimensionalities. According to our study, the proposed methodology can effectively overcome the “curse of dimensionality” and optimally allocate the resources with high accuracy and robustness.

Intelligent Layout Design of Ship Pipeline Using a Particle Swarm Optimisation Integrated Genetic Algorithm

This paper proposes a Particle Swarm Optimisation Integrated Genetic (PSOIG) algorithm to define ship pipeline layout, where the pipeline layout environment is complex and changeable. The pipeline layout space model includes a cabin model, an obstacle model, a pipe model and a regional model of layout. Given the characteristics of ship pipeline layout, the direction guidance mechanism for automatic pipeline layout is introduced, and a direction parameter setting are put forward to further improve the efficiency of the algorithm. At the same time, the crossover and mutation strategies of the genetic algorithm are introduced into the particle swarm optimisation to establish the PSOIG algorithm for ship pipeline intelligent layout. This fully optimises the advantages of particle swarm optimisation and genetic algorithms to improve the diversity of solutions and the convergence speed of the algorithm. Finally, the simulation results demonstrate the feasibility and efficiency of the proposed algorithm.

A New Norm-Observed Calibration Method Based on Improved Differential Evolution Algorithm for SINS

It is vital for a strapdown inertial navigation system (SINS) to be calibrated before normal use. In this paper, a new kind of norm-observed calibration method is proposed. Considering that the norm of the output of accelerometers and gyroscopes can be exactly the norm of local acceleration of gravity and Earth rotation angular velocity, respectively, optimization function about all-parameter calibration and the corresponding 24-position calibration path is established. Differential evolutionary algorithm (DE) is supposed to be the best option in parameter identification due to its strong search and fast convergence abilities. However, the high-dimensional individual vector from calibration error equations restrains the algorithm’s optimum speed and accuracy. To overcome this drawback, improved DE (IDE) optimization is specially designed: First, current “DE/rand/1” and “DE/current-to-best/1” mutation strategies are combined as one with complementary advantages and overall balance during the whole optimization process. Next, with the increase of the evolutionary generation, the mutation factor can adjust itself according to the convergence situation. Multiple identification tests prove that our IDE optimization has rapid convergence and high repeatability. Besides, certain motivation of external angular velocity is added to the gyroscope calibration, and a series of dynamic observation paths is formed, further improving the optimization accuracy. The final static navigation experiment shows that SINS with calibration parameters solved by IDE has better performance over other identification methods, which further explains that our novel method is more accurate and reliable in parameter identification.

Solving open travelling salesman subset-tour problem through a hybrid genetic algorithm

In open travelling salesman subset-tour problem (OTSSP), the salesman needs to traverse a set of k (≤n) out of n cities and after visiting the last city, the salesman does not necessarily return to the central depot. The goal is to minimize the overall traversal distance of covering k cities. The OTSSP model comprises two types of problems such as subset selection and permutation of the cities. Firstly, the problem of selection takes place as the salesman’s tours do not contain all the cities. On the other hand, the next problem is about to determine the optimal sequence of the cities from the selected subset of cities. To deal with this problem efficiently, a hybrid nearest neighbor technique based crossover-free Genetic algorithm (GA) with complex mutation strategies is proposed. To the best of the author’s knowledge, this is the first hybrid GA for the OTSSP. As there are no existing studies on OTSSP yet, benchmark instances are not available for OTSSP. For computational experiments, a set of test instances is created by using TSPLIB. The extensive computational results show that the proposed algorithm is having great potential in achieving better results for the OTSSP. Our proposed GA being the first evolutionary-based algorithm that will help as the baseline for future research on OTSSP.