Local Search Accuracy Research Articles

Design and research of heat dissipation system of electric vehicle lithium-ion battery pack based on artificial intelligence optimization algorithm

This research focuses on the design of heat dissipation system for lithium-ion battery packs of electric vehicles, and adopts artificial intelligence optimization algorithm to improve the heat dissipation efficiency of the system. By integrating genetic algorithms and particle swarm optimization, the research goal is to optimize key design parameters of the cooling system to improve temperature control and extend battery life. In the process of algorithm implementation, genetic algorithm improves the diversity of population through crossover and mutation operations, thus enhancing the global search ability. Particle swarm optimization (PSO) improves local search accuracy and convergence speed by dynamically adjusting inertia weight and learning factor. The effects of different design schemes on heat dissipation performance were systematically evaluated by using computational fluid dynamics (CFD) software. The experimental results show that the efficiency of the cooling system is significantly improved after the application of the optimization algorithm, especially in the aspects of temperature distribution uniformity and maximum temperature reduction. The optimization algorithm also successfully shortens the thermal response time of the system and improves the adaptability and stability of the system under different working conditions. The computational complexity and execution time of these algorithms are also analyzed, which proves the efficiency and feasibility of these algorithms in practical applications. This study demonstrates the practicability and effectiveness of artificial intelligence optimization algorithm in the design of heat dissipation system of lithium-ion battery pack for electric vehicles, and provides valuable reference and practical guidance for the progress of heat dissipation technology of electric vehicles in the future.

An adaptive particle swarm optimization with information interaction mechanism

This paper proposes an adaptive particle swarm optimization with information interaction mechanism (APSOIIM) to enhance the optimization ability of the PSO algorithm. Firstly, a chaotic sequence strategy is employed to generate uniformly distributed particles and to improve their convergence speed at the initialization stage of the algorithm. Then, an interaction information mechanism is introduced to boost the diversity of the population as the search process unfolds, which can effectively interact with the optimal information of neighboring particles to enhance the exploration and exploitation abilities. Therefore, the proposed algorithm may avoid premature and perform a more accurate local search. Besides, the convergence was proven to verify the robustness and efficiency of the proposed APSOIIM algorithm. Finally, the proposed APSOIIM was applied to solve the CEC2014 and CEC2017 benchmark functions as well as famous engineering optimization problems. The experimental results demonstrate that the proposed APSOIIM has significant advantages over the compared algorithms.

An optimization framework for the design of LMUs used in offshore platform float-over installation

Leg mating units (LMU), consisting of steels and hyperelastic materials, are utilized to protect the structures during offshore platform float-over installation. Initial LMU design usually does not satisfy required performance since it is geometrically adjusted to fit given requirements based on previous design. To meet design criteria, design optimization can be applied to the initial design. However, a direct implementation of an accurate local search algorithm may cause heavy computations with the risk of falling into a local optimum. It can be also challenging to find an accurate optimum with efficient global search algorithms due to the characteristics of LMU problems. A small perturbation in nonlinear material can have a significant impact on LMU performance. Therefore, in this research, to find an optimum LMU design efficiently and accurately, an optimization framework that combines global and local search algorithms is implemented. In the framework, an efficient meta-model of optimal prognosis based evolutionary algorithm is first applied. The optimal design obtained from the first step, premature but near the optimum, is set as a new starting point for the sequential optimization where accurate polynomial based local adaptive response surface method is utilized. The robustness of the proposed framework is shown by the development of an LMU suitable for float-over installation of a 20,000-ton topside over an eight-legged fixed offshore jacket platform.

A Chaotic Fish Swarm Algorithm-Based Model for Assessing the Mental Health Status of Older Adults

In this paper, the chaotic fish swarm algorithm is used to conduct in-depth research and analysis on the assessment of the mental health of the elderly. Firstly, the principle, search method, and characteristics of the harmonic search algorithm are analysed, and it is proposed to use the excellent local fine-tuning ability of the harmonic search algorithm to improve the local search accuracy of the artificial fish swarm algorithm. Then, the concept of chaos factor is introduced to improve the global search of the artificial fish swarm algorithm efficiency, using its global search capability without repeated traversal to form a new hybrid fish swarm algorithm. The comparison of experimental results shows that the improved algorithm can effectively guide the robot to avoid obstacles and quickly find the best path or a better path. The improved hybrid algorithm is more efficient and reliable than other algorithms in path planning and can handle more a complex environment model. When considering sample selection bias, ordinary least squares (OLS) regression may underestimate the extent to which social participation affects the mental health of older adults. Further research found that there is heterogeneity in the influence of social participation on the mental health of the elderly. In addition, different types of social participation have different effects on the mental health of the elderly. Simply making friends, physical exercise, and recreational participation in social activities can significantly improve the mental health of the elderly. The improvement is the strongest.

Joint offloading strategy based on quantum particle swarm optimization for MEC-enabled vehicular networks

With the development of the mobile communication technology, a wide variety of envisioned intelligent transportation systems have emerged and put forward more stringent requirements for vehicular communications. Most of computation-intensive and power-hungry applications result in a large amount of energy consumption and computation costs, which bring great challenges to the on-board system. It is necessary to exploit traffic offloading and scheduling in vehicular networks to ensure the Quality of Experience (QoE). In this paper, a joint offloading strategy based on quantum particle swarm optimization for the Mobile Edge Computing (MEC) enabled vehicular networks is presented. To minimize the delay cost and energy consumption, a task execution optimization model is formulated to assign the task to the available service nodes, which includes the service vehicles and the nearby Road Side Units (RSUs). For the task offloading process via Vehicle to Vehicle (V2V) communication, a vehicle selection algorithm is introduced to obtain an optimal offloading decision sequence. Next, an improved quantum particle swarm optimization algorithm for joint offloading is proposed to optimize the task delay and energy consumption. To maintain the diversity of the population, the crossover operator is introduced to exchange information among individuals. Besides, the crossover probability is defined to improve the search ability and convergence speed of the algorithm. Meanwhile, an adaptive shrinkage expansion factor is designed to improve the local search accuracy in the later iterations. Simulation results show that the proposed joint offloading strategy can effectively reduce the system overhead and the task completion delay under different system parameters.

Improved Hybrid Block-Based Motion Estimation for Inter-frame Coding

Digital video technology has been increasingly needed in various fields, such as telecommunications, entertainment, medicine. Therefore, video compression is required. Motion estimation methods help in improving video compression efficiency by effectively removing the temporal redundancy between successive frames. Several block-based motion estimation (BME) algorithms are being suggested to reduce the coding process’s computational complexity. This paper proposes a new rapid hybrid (BME) algorithm established on the primary search point prediction and advance ending search point strategies. It combines rough adaptive search and effective local search. The coarse search introduces a new motion vector (MV) prediction technique that utilizes the macro-blocks (MBs) Spatio-temporal correlations to optimize the traditional adaptive-rood-pattern search algorithm (ARPS) and speeding up the whole process without affecting the accuracy. In the accurate local search, the cross-formed search pattern using a one-step search (OSS) block matching algorithm is employed, to estimate the actual (MV) with less computation time and further speed up the search efficiency. Exhaustive experiments are performed to demonstrate the present algorithm’s performance over the benchmark schemes concerning specific assessment criteria for results, including the peak signal-to-noise ratio (PSNR), computational complexity and computational gain. The results show that the proposed algorithm is efficient and reliable; it can always give better performance over diamond search (DS) and (ARPS). The conducted test shows an increased performance of search speed while preserving the visual quality of the motion-compensated images, and it achieves 59.76–88.03 speed improvement over (DS) and 20.98–72.06 over (ARPS) for different video sequences. Besides, the suggested method (ARP-OSS) provides the best result compared to (DS) and (ARPS) in terms of time complexity for analyzing all the video samples.

A bat optimization algorithm with moderate orientation and perturbation of trend

In order to improve the convergence speed and optimization accuracy of the bat algorithm, a bat optimization algorithm with moderate optimal orientation and random perturbation of trend is proposed. The algorithm introduces the nonlinear variation factor into the velocity update formula of the global search stage to maintain a high diversity of bat populations, thereby enhanced the global exploration ability of the algorithm. At the same time, in the local search stage, the position update equation is changed, and a strategy that towards optimal value modestly is used to improve the ability of the algorithm to local search for deep mining. Finally, the adaptive decreasing random perturbation is performed on each bat individual that have been updated in position at each generation, which can improve the ability of the algorithm to jump out of the local extremum, and to balance the early global search extensiveness and the later local search accuracy. The simulating results show that the improved algorithm has a faster optimization speed and higher optimization accuracy.

Hybridization of Multi-Objective Deterministic Particle Swarm with Derivative-Free Local Searches

The paper presents a multi-objective derivative-free and deterministic global/local hybrid algorithm for the efficient and effective solution of simulation-based design optimization (SBDO) problems. The objective is to show how the hybridization of two multi-objective derivative-free global and local algorithms achieves better performance than the separate use of the two algorithms in solving specific SBDO problems for hull-form design. The proposed method belongs to the class of memetic algorithms, where the global exploration capability of multi-objective deterministic particle swarm optimization is enriched by exploiting the local search accuracy of a derivative-free multi-objective line-search method. To the authors best knowledge, studies are still limited on memetic, multi-objective, deterministic, derivative-free, and evolutionary algorithms for an effective and efficient solution of SBDO for hull-form design. The proposed formulation manages global and local searches based on the hypervolume metric. The hybridization scheme uses two parameters to control the local search activation and the number of function calls used by the local algorithm. The most promising values of these parameters were identified using forty analytical tests representative of the SBDO problem of interest. The resulting hybrid algorithm was finally applied to two SBDO problems for hull-form design. For both analytical tests and SBDO problems, the hybrid method achieves better performance than its global and local counterparts.

Research on hybrid optimization and deep learning modeling method in the performance seeking control

A novel performance seeking control method based on hybrid optimization algorithm and deep learning modeling method is proposed to get a better engine performance. The deep learning modeling method, deep neural network, which has strong representation capability and can deal with big training data, is adopted to establish an on-board engine model. A hybrid optimization algorithm—genetic algorithm particle swarm optimization–feasible sequential quadratic programming—is proposed and applied to performance seeking control. The genetic algorithm particle swarm optimization–feasible sequential quadratic programming not only has the global search ability of genetic algorithm particle swarm optimization, but also has the high local search accuracy of feasible sequential quadratic programming. The final simulation experiments show that, compared with feasible sequential quadratic programming, genetic algorithm particle swarm optimization, and genetic algorithm, the proposed optimization algorithm can get more installed thrust, decrease fuel consumption between 2% to 3%, and decrease turbine blade temperature larger than 15k, while meeting all of the constraints. Moreover, it also shows that the proposed modeling method has high accuracy and real-time performance.

A quantum-behaved particle swarm optimization algorithm with the flexible single-/multi-population strategy and multi-stage perturbation strategy based on the characteristics of objective function

The characteristics of objective functions have important impacts on the search process of the optimization algorithm. Many multimodal functions tend to make the algorithm fall into local optima, and the local search accuracy is usually affected by the coupling of the objective functions in different dimensions. A novel quantum-behaved particle swarm optimization algorithm with the flexible single-/multi-population strategy and the multi-stage perturbation strategy (QPSO_FM) is proposed in the present paper. This algorithm aims to adjust the optimization strategies based on the characteristics of the objective functions. The number of sub-populations is determined by the monotonicity variations of the objective functions, and two mechanisms are introduced to balance the diversity and the convergent speed for the multi-population case. The strategy of multi-stage perturbation is applied to enhance the search ability. At the first stage, the main target of the perturbation is to broaden the search range. The second stage applies the univariate perturbation (relying on the coupling degree of the objective function) to raise the local search accuracy. Performance comparisons between the proposed and existing algorithms are carried out through the experiments on the standard functions. The results show that the proposed algorithm can generally provide excellent global search ability and high local search accuracy.

A DW-SAPSO Localization Method based on Correctional RSSI Ranging Model

Aiming at the problem of low localization accuracy caused by the ranging error generated by the RSSI ranging model with empirical parameters in different indoor scenarios, a simulated annealing-based particle swarm optimization localization algorithm with decreasing inertia weight (DW-SAPSO) is proposed. In the RSSI ranging phase, the parameters of ranging model are estimated and corrected by the difference between different reference points. In the localization phase, the nonlinear cost function is constructed according to the measurable quantity. Simultaneously the simulated annealing and decreasing inertia weight mechanism are introduced to the standard particle swarm optimization (PSO) localization algorithm, which effectively improves the algorithm’s global optimization ability and local search accuracy. Experimental results show that compared with an existing method and the PSO localization algorithm, the modified DW-SAPSO localization algorithm has better convergence performance and can improve the average localization accuracy of the latter by about 32.7%, which has some practical value.

An improved adaptive human learning algorithm for engineering optimization

Human learning Optimization (HLO) is an emergent promising meta-heuristic algorithm which uses the random learning operator, the individual learning operator, and the social learning operator to search out the optimal solution of problems based on a simplified human learning model. To enhance the search performance, a new improved adaptive human learning optimization algorithm (IAHLO) is proposed in this paper, in which a novel adaptive strategy is developed to dynamically tune the control parameter of the random learning operator so that IAHLO can efficiently fasten the convergence at the beginning of iteration, develop the diversity at the middle of searching process to better explore the solution space, and perform the accurate local search at the end of search to find the optima. The presented IAHLO is applied to solve well-known engineering optimization problems and its results are compared with those of the standard HLO, adaptive HLO variants, and other metaheuristics to evaluate its performance. The comparison results show that IAHLO outperforms the other algorithms and achieves the best results on the engineering problems as the improved adaptive mechanism possesses the advantages of the linearly increasing and decreasing strategies and avoids their disadvantages.

An optimal solution for software testing case generation based on particle swarm optimization

AbstractSearching based testing case generation technology converts the problem of testing case generation to function optimizations, through a fitness function, which is usually optimized using heuristic search algorithms. The particle swarm optimization (PSO) optimized testing case generation algorithm tends to lose population diversity of locally optimal solutions with low accuracy of local search. To overcome the above defects, a self-adaptive PSO based software testing case optimization algorithm is proposed. It adjusts the inertia weight dynamically according to the current iteration and average relative speed, to improve the performance of standard PSO. An improved alternating variable method is put forward to accelerate local search speed, which can coordinate both global and local search ability thereby improving the overall generation efficiency of testing cases. The experimental results demonstrate that the approach outlined here keeps higher testing case generation efficiency, and it shows certain advantages in coverage, evolution generation amount and running time when compared to standard PSO and GA-PSO.

Hierarchical genetic-particle swarm optimization for bistable permanent magnet actuators

Bistable permanent magnet actuator (BPMA) has been widely used in industry as a kind of energy converter. However, its power consumption and response performance are in conflict during operating because of the conflict between holding force and initial force (the minimum threshold driving force). An optimization procedure based on a novel hierarchical genetic-particle swarm algorithm (HGP) for a hybrid excited linear actuator (HELA) constrained in a specific volume has been developed. For obtaining the objective function for each parameter combination precisely, three-dimensional finite element analysis (FEA) was employed. Meanwhile, the improvement of optimization efficiency and quality is extremely demanding because the FEA needs much computing time. The proposed hybrid algorithm adopts a hierarchical structure from the concept of “experimental classes” in China. The base level is composed of the majority general individuals of Genetic algorithms (GA), which provides the global search ability of the entire algorithm. The top level comprises the minority elite consisting of the better individual, which performs the accurate local search by using the particle swarm optimization (PSO) algorithm with variable inertia weight and constriction coefficient. Additionally, the performance of HGP has been evaluated through the Rosenbrock function, and the proposed method is superior to other related methods Finally, the proposed procedure was verified by optimizing HELA's parameters in multidimensional parameters space under the design constraint. The results show that both of the holding force and the initial force are improved more than 25% compared with the initial design, which ensures both low power consumption and fast response.

Multi-objective particle optimization algorithm based on sharing–learning and dynamic crowding distance

A multi-objective particle swarm optimization algorithm, based on share–learning and dynamic crowding distance (MOPSO-SDCD), is proposed to improve the convergence accuracy and keep the diversity of the Pareto optimal solutions. First, the sharing–learning factor is applied to modify the velocity updating formulas, which improves both the global search ability and local search accuracy of the algorithm. Meanwhile, Gaussian mutation and greedy strategy are adopted to update personal best position and external archive, which make the algorithm approximate the Pareto front quickly and avoid premature convergence. Finally, MOPSO-SDCD maintains the external archive based on dynamic crowding distance sorting strategy, whose purpose is boosting the diversity and distribution of Pareto optimal solutions. The ZDT series test functions are used to test the performance of MOPSO-SDCD and compare with other three typical algorithms. Simulation results verify the superiority and effectiveness of the proposed algorithm.

Improved DV-Hop Localization Algorithm Based on Bat Algorithm in Wireless Sensor Networks

Obtaining accurate location information is important in practical applications of wireless sensor networks (WSNs). The distance vector hop (DV-Hop) is a frequently-used range-free localization algorithm in WSNs, but it has low localization accuracy. Moreover, despite various improvements to DV-Hop-based localization algorithms, maintaining a balance between high localization accuracy and good stability and convergence is still a challenge. To overcome these shortcomings, we proposed an improved DV-Hop localization algorithm based on the bat algorithm (IBDV-Hop) for WSNs. The IBDV-Hop algorithm incorporates optimization methods that enhance the accuracy of the average hop distance and fitness function. We also introduce a nonlinear dynamic inertial weight strategy to extend the global search scope and increase the local search accuracy. Moreover, we develop an updated solutions strategy that avoids premature convergence by the IBDV-Hop algorithm. Both theoretical analysis and simulation results show that the IBDV-Hop algorithm achieves higher localization accuracy than the original DV-Hop algorithm and other improved algorithms. The IBDV-Hop algorithm also exhibits good stability, search capability and convergence, and it requires little additional time complexity and energy consumption.

Novel prediction and memory strategies for dynamic multiobjective optimization

Dynamic multiobjective optimization problems (DMOPs) exist widely in real life, which requires the optimization algorithms to be able to track the Pareto optimal solution set after the change efficiently. In this paper, novel prediction and memory strategies (PMS) are proposed to solve DMOPs. Regarding prediction, the prediction strategy contains two parts, i.e., exploration and exploitation. Exploration can enhance the ability to search the entire solution space, making it adapt to the environmental change with a great extent. Exploitation can improve the accuracy of local search, making the algorithm to have a faster response to environmental change particularly in the solution set having relevance in the environment. In terms of memory, an optimal solution set preservation mechanism is employed, by reusing the previously found elite solutions, which improves the performance of the algorithm in solving periodic problems. Compared with two representative prediction strategies and a hybrid strategy combining prediction and memory both on seven traditional benchmark problems and on five newly appeared ones, PMS has been shown to have faster response to the environmental changes than the peer algorithms, performing well in terms of convergence and diversity.

CABC–CSA: a new chaotic hybrid algorithm for solving optimization problems

Recently a lot of methods have been presented for solving optimization problems. In this paper, we are trying to propose a new hybrid algorithm for solving these kinds of problem. The proposed algorithm is based on chaotic artificial bee colony and chaotic simulated annealing, CABC–CSA. The chaotic artificial bee colony finds new locations chaotically. Actually, the proposed algorithm provides a combination of local search accuracy of simulated annealing and the ability of global search of artificial bee colony. Furthermore, we used a different method for generating the initial population. The proposed algorithm is validated using 12 benchmark functions. The results are compared with those of the artificial bees’ algorithm, the hybrid algorithm of artificial bee colony and simulated annealing and particle swarm optimization. Simulation results show the efficiency of the proposed algorithm.

Combining Genetic Algorithm and Generalized Least Squares for Geophysical Potential Field Data Optimized Inversion

A genetic algorithm (GA) and generalized least squares (GLS)-based approach, hereafter called GA-GLS, is proposed to solve geophysical optimized inversion. In this method, GA is exploited to initialize nonlinear parameter estimation, and GLS is used for accurate local search. Here, we compare the results from GA, GLS, and proposed GA-GLS to invert the synthesized potential field. The results show that GA-GLS outperforms GA in terms of accuracy, as well as GLS, which needs given initial parameters. The real data are taken to verify the feasibility of implementing it in practice.

Deriving operating policies for multi-objective reservoir systems: Application of Self-Learning Genetic Algorithm

Optimal multi-reservoir operation is a multi-objective problem in nature and some of its objectives are nonlinear, non-convex and multi-modal functions. There are a few areas of application of mathematical optimization models with a richer or more diverse history than in reservoir systems optimization. However, actual implementations remain limited or have not been sustained. Genetic Algorithms (GAs) are probabilistic search algorithms that are capable of solving a variety of complex multi-objective optimization problems, which may include non-linear, non-convex and multi-modal functions. GA is a population based global search method that can escape from local optima traps and find the global optima. However GAs have some drawbacks such as inaccuracy of the intensification process near the optimal set. In this paper, a new model called Self-Learning Genetic Algorithm (SLGA) is presented, which is an improved version of the SOM-Based Multi-Objective GA (SBMOGA) presented by Hakimi-Asiabar et al. (2009) [45]. The proposed model is used to derive optimal operating policies for a three-objective multi-reservoir system. SLGA is a new hybrid algorithm which uses Self-Organizing Map (SOM) and Variable Neighborhood Search (VNS) algorithms to add a memory to the GA and improve its local search accuracy. SOM is a neural network which is capable of learning and can improve the efficiency of data processing algorithms. The VNS algorithm can enhance the local search efficiency in the Evolutionary Algorithms (EAs). To evaluate the applicability and efficiency of the proposed methodology, it is used for developing optimal operating policies for the Karoon-Dez multi-reservoir system, which includes one-fifth of Iran's surface water resources. The objective functions of the problem are supplying water demands, generating hydropower energy and controlling water quality in downstream river.