Opposition-based Magnetic Optimization Algorithm with parameter adaptation strategy

Adaptive parameter control is a proven method to increase efficiency of any optimisation algorithm and have has been universally proven on various benchmark problems. In this research report, a Universal model free method of adaptive control of optimisation parameters was proposed. The goal of the research is to explore the method that can be used for any model of optimisation algorithm (‘Swiss army knife’ approach). The proposed method utilises a score system in which points are rewarded for improvement found on individual agent performance while non-improving will experience a score-decay. The proposed method was tested on two types of common optimisation algorithm, Truncated Lěvy flight and the Differential Evolution (DE). This Adaptive parameter strategy is solely based on search agent's performance and hence is not algorithm specific (model free). As a preliminary study, this report will only focus on Lower Dimensional problems (D=10) using CEC 2014 benchmark test sets. The results showed that it work well on DE but gave mediocre results for Le $\check{v}$ y Flight when compared with their static parameter counterparts. This showed performance varies across algorithms types but verified the adaptive capability of the proposed method.

In order to solve problems with the original crayfish optimization algorithm (COA), such as reduced diversity, local optimization, and insufficient convergence accuracy, a multi-strategy optimization algorithm for crayfish based on differential evolution, named the ICOA, is proposed. First, the elite chaotic difference strategy is used for population initialization to generate a more uniform crayfish population and increase the quality and diversity of the population. Secondly, the differential evolution strategy and the dimensional variation strategy are introduced to improve the quality of the crayfish population before its iteration and to improve the accuracy of the optimal solution and the local search ability for crayfish at the same time. To enhance the updating approach to crayfish exploration, the Levy flight strategy is adopted. This strategy aims to improve the algorithm's search range and local search capability, prevent premature convergence, and enhance population stability. Finally, the adaptive parameter strategy is introduced to improve the development stage of crayfish, so as to better balance the global search and local mining ability of the algorithm, and to further enhance the optimization ability of the algorithm, and the ability to jump out of the local optimal. In addition, a comparison with the original COA and two sets of optimization algorithms on the CEC2019, CEC2020, and CEC2022 test sets was verified by Wilcoxon rank sum test. The results show that the proposed ICOA has strong competition. At the same time, the performance of ICOA is tested against different high-performance algorithms on 6 engineering optimization examples, 30 high-low-dimension constraint problems and 2 large-scale NP problems. Numerical experiments results show that ICOA has superior performance on a range of engineering problems and exhibits excellent performance in solving complex optimization problems.

Whale optimization algorithm (WOA) is a relatively novel swarm-based optimization algorithm that has the advantage of less control parameters, strong global optimization ability and easy of implementation. However, there is still an insufficiency of low accuracy, premature, and slow convergence speed in tackling large-scale global optimization (LSGO) problems. To address the issue, an enhanced whale optimization algorithm (EWOA), which combines the original WOA with opposition-based learning, chaotic map and dynamic inertia weight, is proposed for solving LSGO problems. Chaotic opposition-based learning initialization strategy by comparing the fitness of an individual to its opposite and retaining well-diversified solutions is employed to accelerate convergence speed and improve the exploration. In addition, in order to make full use of and balance the exploration and exploitation of the original WOA, a novel chaotic cooperative updating strategy of nonlinear control parameter and dynamic inertia weight is given. Moreover, optimal individual chaotic searching strategy is integrated with evolutionary population updating to avoid the possibility of being trapped into local optima. The experimental results on four classical benchmark functions with dimensions ranging from 100 to 1000 demonstrate the effectiveness and efficiency of the chaotic opposition-based learning initialization strategy, chaotic nonlinear control parameter updating strategy and optimal individual chaotic searching strategy. The comparisons show that the proposed EWOA significantly improves the performance of WOA and provides competitive results for LSGO problems compared with state-of-the-art algorithms.

This paper proposes a new adaptive fuzzy nonlinear control strategy that allows designers to systematically construct fuzzy control. The control strategy proposes the use of fuzzy logic systems within a well understood control structure that is called nonlinear internal model control (NIMC) structure. The attractive feature of the NIMC structure is that the relations between some designed parameters and the performance of the control system can be found explicitly. Thus, this control structure allows designers to systematically construct fuzzy control. In addition to using fuzzy logic systems within a well understood control structure, an adaptive control strategy is also proposed in this paper. The control strategy is applied to control the inverted pendulum model. The results show that the proposed strategy can maintain the system stability and give a good control performance.

ABSTRACTIn the field of optimisation, the accurate configuration of parameters in metaheuristic algorithms is a critical yet often arduous task that significantly impacts the efficiency and efficacy of the search process. This study was motivated by the need to address the inefficiencies and limitations associated with conventional methods of parameter configuration, which typically involve manual, trial‐and‐error approaches. These traditional methods can lead to suboptimal performance and increased computational overhead. To tackle these challenges, this study introduces a novel adaptive parameter control strategy for parallel island‐based metaheuristics, with a particular emphasis on the ant colony optimisation (ACO) algorithm. Our research process involved extensive experimentation to evaluate the effectiveness of this adaptive strategy. We conducted a series of tests to enable real‐time adjustment of key parameters based on the performance of ACO colonies, thereby enhancing both exploration and exploitation capabilities. The results indicate that the adaptive strategy consistently outperforms offline manual and automated tuning configurations, particularly in larger and more complex problem instances, providing a more efficient solution for parameter optimisation in metaheuristics. These findings highlight the potential of dynamic parameter control to reduce dependency on expert knowledge and manual tuning while improving algorithmic performance.

Nonlinear Adaptive Control for Turning Processes

Recently, transformable Unmanned Aerial Vehicles (UAVs) have become a subject of great interest in the field of flying systems, due to their maneuverability, agility and morphological capacities. They can be used for specific missions and in more congested spaces. Moreover, this novel class of UAVs is considered as a viable solution for providing flying robots with specific and versatile functionalities. In this paper, we propose (i) a new design of a transformable quadrotor with (ii) generic modeling and (iii) adaptive control strategy. The proposed UAV is able to change its flight configuration by rotating its four arms independently around a central body, thanks to its adaptive geometry. To simplify and lighten the prototype, a simple mechanism with a light mechanical structure is proposed. Since the Center of Gravity (CoG) of the UAV moves according to the desired morphology of the system, a variation of the inertia and the allocation matrix occurs instantly. These dynamics parameters play an important role in the system control and its stability, representing a key difference compared with the classic quadrotor. Thus, a new generic model is developed, taking into account all these variations together with aerodynamic effects. To validate this model and ensure the stability of the designed UAV, an adaptive backstepping control strategy based on the change in the flight configuration is applied. MATLAB simulations are provided to evaluate and illustrate the performance and efficiency of the proposed controller. Finally, some experimental tests are presented.

Wind power installations are rapidly increasing worldwide, leading to a huge level of permeation into electricity supply networks. Enormous efforts are spent to improve the performance of the wind turbine generator systems. This paper proposes a novel adaptive fuzzy logic control strategy for performance improvement of a grid-tied wind generator system. The variable-speed wind turbine driven permanent-magnet synchronous generator is tied to the electricity network by a full-capacity power converter. A cascaded adaptive fuzzy logic control strategy is proposed as the control methodology for the generator- and the grid-side converter/inverter. The adaptive technique depends on a continuous mixed p-norm algorithm, which on-line updates the scaling factors of the fuzzy logic controllers (FLCs) at a high convergence speed. For the sake of preciseness, real wind speed data measured in the Zaafarana wind farm, Egypt, are considered in the analyses. The effectiveness of the proposed adaptive FLC is compared to that achieved using particle swarm optimization algorithm based an optimal proportional-integral controller, considering severe grid disturbances. Extensive simulation analyses, which are done using MATLAB/Simulink software, are presented to validate the efficiency of the adaptive fuzzy logic control strategy.

A decomposition framework based on memorized binary search for large-scale optimization problems

Although there are many studies on large-scale unconstrained optimization (e.g., with 100 to 1000 variables) and small-scale constrained optimization (e.g., with 10 to 30 variables) using nature-inspired algorithms (e.g., evolutionary algorithms and swarm intelligence algorithms), no publicly available nature-inspired algorithm is developed for large-scale constrained optimization. In this paper, we combine a cooperative coevolutionary particle swarm optimization (CCPSO) algorithm with the e constrained method to solve large-scale real-valued constrained optimization problems. The eCCPSO framework is proposed, and three different algorithms based on the framework, i.e., eCCPSOd, eCCPSOw and eCCPSOw2, are developed. The proposed algorithms compare favorably to the state-of-the-art constrained optimization algorithm eDEag on large-scale problems. The experimental results further suggest that eCCPSOw2 with adaptive improvement detection technique is highly competitive compared with the other algorithms considered in this work for solving large-scale real-valued constrained optimization problems.

Compactly supported radial basis functions for adaptive process control

Recently, the large-scale optimization problems have become a common research topic in the field of evolutionary computation. It is hard to find optimal solutions when solving large-scale multi-objective optimization problems (LSMOPs), due to the ineffectiveness of existing operators. In the other word, the search ability of most existing MOEAs on solving LSMOPs is still weak. To address this issue, an efficient competitive swarm optimizer with a strong exploration ability, denoted as E-CSO, is presented in this paper, which designs a novel three-particle-based particle updating strategy to improve the search efficiency. The experimental results validate the high efficiency and effectiveness of our proposed approach when solving various LSMOPs.

Conventional Particle Swarm Optimization (PSO) algorithms often suffer premature convergences. Hybrid algorithms, for instance, the Simulated Annealing-based PSO, present low convergence speeds. In this paper, we develop an Adaptive Particle Swarm Optimization (APSO) algorithm to solve unconstrained global optimization problems with highly multimodal functions, in which two adaptive strategies (including an adaptive inertia weight strategy with hybrid time-varying dynamics and an adaptive random mutation strategy) are merged into the basic PSO algorithm to guarantee the algorithm performance. The proposed algorithm is numerically verified by fifteen classical multimodal functions which include Ackley, Bukin f6, Cross-in-Tray, Drop-Wave, Eggholder, Griewank, Holder Table, Langermann, Levy, Levy f13, Rastrigin, Schaffer f2, Schaffer f4, Schwefel, and Shubert. Numerical experiments demonstrate that the proposed algorithm has a potential to achieve better solutions with acceptable computational time, especially for high-dimensional optimization problems.

With the increase of on-orbit maintenance and support requirements, the application of a space manipulator is becoming more promising. In actual operation, the strong coupling of the free-floating space robot itself and the unknown disturbance of the contact target caused a major challenge to the robot base posture control. Traditional Reaction Null Space (RNS) motion planning and control methods require the construction of precise dynamic models, which is impossible in reality. In order to solve this problem, this paper proposes a new Adaptive Reaction Null Space (ARNS) path planning and control strategy for the contact of free-floating space robots with unknown targets. The ARNS path planning strategy is constructed by the Variable Forgetting Factor Recursive Least Squares (VFF–RLS) algorithm. At the same time, a robust adaptive control strategy based on the Strategy Self-Adaption Differential Evolution–Extreme Learning Machine (SSADE–ELM) algorithm is proposed to track the dynamic changes of the planned path. The algorithm enables us to intelligently learn and compensate for the unknown disturbance. Then, this paper constructs a robust controller to compensate model uncertainty. A striking feature of the proposed strategy is that it does not require an accurate system model or any information about unknown attributes. This design can dynamically implement RNS path tracking performance. Finally, through simulation and experiment, the proposed algorithm is compared with the existing methods to prove its effectiveness and superiority.

This paper presents an adaptive predictive control strategy based on the Laguerre function sequence for the Chopper cascade speed control system. The principles and algorithms for this adaptive control strategy are introduced briefly. The simulation results of the chopper cascade speed control system demonstrated that the design of the strategy does not rely on accurate mathematical models. The strategy has not only the property of a faster response, smaller overshoot, but also a better disturbance rejection capability than PID controllers.