An Elastic Collision Seeker Optimization Algorithm for Optimization Constrained Engineering Problems

To improve the seeker optimization algorithm (SOA), an elastic collision seeker optimization algorithm (ECSOA) was proposed. The ECSOA evolves some individuals in three situations: completely elastic collision, completely inelastic collision, and non-completely elastic collision. These strategies enhance the individuals’ diversity and avert falling into the local optimum. The ECSOA is compared with the particle swarm optimization (PSO), the simulated annealing and genetic algorithm (SA_GA), the gravitational search algorithm (GSA), the sine cosine algorithm (SCA), the multiverse optimizer (MVO), and the seeker optimization algorithm (SOA); then, fifteen benchmark functions, four PID control parameter models, and six constrained engineering optimization problems were selected for the experiment. According to the experimental results, the ECSOA can be used in the benchmark functions, the PID control parameter optimization, and the optimization constrained engineering problems. The optimization ability and robustness of ECSOA are better.

A Complex-Valued Encoding Multichain Seeker Optimization Algorithm for Engineering Problems

This article comes up with a complex-valued encoding multichain seeker optimization algorithm (CMSOA) for the engineering optimization problems. The complex-valued encoding strategy and the multichain strategy are leaded in the seeker optimization algorithm (SOA). These strategies enhance the individuals’ diversity, enhance the local search, avert falling into the local optimum, and are the influential global optimization strategies. This article chooses fifteen benchmark functions, four proportional integral derivative (PID) control parameter models, and six constrained engineering problems to test. According to the experimental results, the CMSOA can be used in the benchmark functions, in the PID control parameter optimization, and in the optimization of constrained engineering problems. Compared to the particle swarm optimization (PSO), simulated annealing based on genetic algorithm (SA_GA), gravitational search algorithm (GSA), sine cosine algorithm (SCA), multiverse optimizer (MVO), and seeker optimization algorithm (SOA), the optimization ability and robustness of the CMSOA are better than those of others algorithms.

A Complex-valued Encoding Seeker Optimization Algorithm for Constrained Engineering Problems

This article comes up with a complex-valued encoding seeker optimization algorithm (CSOA) base on the multi-chain method for the constrained engineering optimization problems. The complex value encoding and a multi-link strategy are leaded by the seeker optimization algorithm (SOA). The complex value encoding method is an influential global optimization strategy, and the multi-link is an enhanced local search strategy. These strategies enhance the individuals’ diversity and avert fall into the local optimum. This article chose fifteen benchmark functions, four PID control parameter models, and six constrained engineering problems to test. According to the experimental results, the CSOA algorithm can be used in the benchmark functions, PID control parameters optimization, and optimization constrained engineering problems. Compared to particle swarm optimization (PSO), simulated annealing base on genetic algorithm (SA_GA), gravitational search algorithm (GSA), sine cosine algorithm (SCA), multi-verse optimizer (MVO), and seeker optimization algorithm (SOA), the optimization ability and robustness of CSOA are better.

Optimal control strategy of state feedback control for surface-mounted PMSM drives based on auto-tuning of seeker optimization algorithm

This paper focus on a state feedback controller (SFC)-based optimal control scheme for surface-mounted permanent-magnet synchronous motor (SPMSM) with auto-tuning of controller built on seeker optimization algorithm (SOA). First, based on the nonlinear state-space model of SPMSM, voltage feedforward compensation is used to design a linear SFC. Then in order to guarantee the steady performance in speed and current, integral models considering the errors of rotor speed and current response in d-axis are added in the state space model of SPMSM. Furthermore, by statically decoupling the load torque in the state equation, feedforward compensation is implemented on the load torque to improve the dynamic performance of the controller. The load torque is estimated using disturbance observer with reasonable parameter selection. Then, with the consideration of the search capacity of seeker optimization algorithm (SOA), it is adopted to acquire matrix coefficient of the presented controller. Furthermore, in order to suppress the speed overshoot, a penalty term is introduced to the fitness index. The performance of the proposed method has been validated experimentally and compared with the conventional method under different conditions.

The control design of transverse interconnected electronic control air suspension based on seeker optimization algorithm

In this paper, in the light of the problems of the traditional air suspension PID controller in the process of body height adjustment, such as the adjustment time is too long, the overshoot phenomenon is obvious, and the control parameters cannot be adjusted in real time, a PID transverse interconnected electronic control air suspension(TIECAS) system controller based on seeker optimization algorithm (SOA) is designed, the proportion factor of PID is optimized by crowd search algorithm and get the optimal solution of PID controller parameters. The control system model is built in [Formula: see text] simulation software. The simulation results show that the PID lateral interconnected air suspension controller based on SOA has faster response and avoids overshoot than the traditional PID controller. The control system was tested on a self-developed test vehicle with TIECAS structure. The test results show that the root mean square(RMS) values of the roll angle and pitch angle of the test vehicle are reduced from [Formula: see text] and [Formula: see text] before control to [Formula: see text] and [Formula: see text], respectively, by [Formula: see text] and [Formula: see text]. The RMS values of the vertical acceleration of the center of mass after control are reduced by [Formula: see text] and [Formula: see text] compared with that without control, effectively improve the ride comfort and operation stability of the vehicle, The research results provide a new idea for the control of the vehicle transverse interconnected electronic air suspension system.

Power Distribution Systems Using Bit-Shift Operator Based MOSOA

This paper presents a new approach based on the application of a bit-shift operator based multi-objective seeker-optimization-algorithm (BS-MOSOA) for designing of combined primary and secondary power distribution system (CPDS) considering both system cost and reliability. In number of researches works the planning of secondary power distribution system (PDS) has not been considered for planning strategy. However, it is observed that the total investment and operational cost components of secondary PDS plays an important role in the overall system cost. Thus, in this proposed research a CPDS has been considered for comprehensive planning of complex PDS. Furthermore, a reliability index called Contingency-load-loss-index is used for the reliability assessment of the network. The algorithm uses a modified version of seeker-optimization-algorithm (SOA), which is based on the status of changing switches and shift operator to generate a group of non-dominated solutions. Also, fuzzy theory approach is used for selection of most suitable solution among the non-dominated solutions from the obtained Pareto-front. The proposed method is illustrated on a real test case consist of a residential primary and secondary network of 75 electrical nodes. Furthermore, a qualitative comparison is made with existing traditional and classical methodologies, to show the efficacy of the proposed planning approach.

Fractional-Order Modeling and Parameter Identification for Ultracapacitors with a New Hybrid SOA Method

This paper deals with an ultracapacitor (UC) model and its identification procedure. To take UC’s fractional characteristic into account, two constant phase elements (CPEs) are used to construct a model structure according to impedance spectrum analysis. The different behaviors of UC such as capacitance, resistance, and charge distribution dynamics are simulated by the corresponding part in the model. The resistance under different voltages is calculated through the voltage rebound method to explore its non-linear characteristics and create a look-up table. A nonlinear fractional model around an operation voltage is then deduced by applying the resistance table. This time identification is carried by a proposed hybrid optimization algorithm: Nelder-Mead seeker algorithm (NMSA), which embeds the Nelder–Mead Simplex (NMS) method into the seeker optimization algorithm (SOA). Its time behavior has been compared with the linear fractional model for charging and discharging current profiles at different levels.