Multi-peak Optimization Problems Research Articles

Reinforcement Learning-Enabled Resampling Particle Swarm Optimization for Sensor Relocation in Reconfigurable WSNs

Aiming to maximize coverage performance and reduce the number of sensors deployed in the reconfigurable wireless sensor networks (RWSNs), in this paper, we first formulate a new cooperative sensing coverage control problem based on the confident information coverage model. Then, inspired by the reinforcement learning and resampling technology, a novel learning automata-based resampling particle swarm optimization (RPSOLA) algorithm is proposed to solve complex multi-peak optimization problem and optimize the cooperative sensing coverage control problem of RWSNs. Experimental results demonstrate that the RPSOLA considerably outperforms other three peer schemes, the RPSO, BASPSO and PSO, in terms of the convergence, coverage rate and sensor redundancy.

A Novel Chaotic Fractional-Order Beetle Swarm Optimization Algorithm and Its Application for Load-Frequency Active Disturbance Rejection Control

This brief proposes a novel chaotic fractional-order beetle swarm optimization (CFBSO) algorithm that combines chaos concepts and a fractional derivative structure with the beetle swarm algorithm. The proposed CFBSO was compared with other advanced meta-heuristic algorithms on 23 benchmark functions with single-peak, multi-peak, and fixed-dimensional multi-peak optimization problems, and the effectiveness and superiority of the proposed algorithm were verified. The proposed CFBSO was then used to optimize the parameters of the active disturbance rejection controller (ADRC). The CFBSO-based ADRC was further applied for the load frequency control (LFC) system of a four-area interconnected power system consisting of a hydraulic turbine with a non-minimum phase (NMP) and three reheat turbines. The results showed that the proposed method had a smaller undershoot and shorter settling time than those obtained by a linear ADRC and a proportional–integral–derivative controller. Thus, it can meet the high-performance requirements of LFC.

Photonic Band Gap Material Topological Design at Specified Target Frequency (Adv. Theory Simul. 10/2021)

Photonic Crystals Optimization at Specified Target Frequency To achieve any specified band gap photonic crystal (PhC) structure around the target frequency, a new band gap measuring index and topological design strategy are reported in article number 2100125 by Xiaopeng Zhang, Yangjun Luo, Yi Yan, and co-workers. The cell topology is represented with only 50 design variables through the material-field series-expansion, and the multi-peak optimization problem is solved by the sequential Kriging-based algorithm. The cover shows an ultra-wide band gap PhC device.

PSO-ACSC: a large-scale evolutionary algorithm for image matting

Image matting is an essential image processing technology due to its wide range of applications. Sampling-based image matting is one of the main branches of image matting research that estimates alpha mattes by selecting the best pixel pairs. It is essentially a large-scale multi-peak optimization problem of pixel pairs. Previous study shows that particle swarm optimization (PSO) can effectively optimize the pixel pairs. However, it still suffers from premature convergence problem which often occurs in pixel pair optimization that involves a large number of local optima. To address this problem, this work presents a parameter-free strategy for PSO called adaptive convergence speed controller (ACSC). ACSC monitors and conditionally controls the particles by competitive pixel pair recombination operator (CPPRO) and pixel pair reset operator (PPRO) during the iteration. ACSC performs CPPRO to improve the competitiveness of a particle when the performance of most of the pixel pairs is worse than that of the best-so-far solution. PPRO is performed to avoid premature convergence when the alpha mattes regarding two selected particles are highly similar. Experimental results show that ACSC significantly enhances the performance of PSO for image matting and provides competitive alpha mattes comparing with state-of-the-art evolutionary algorithms.

Coverage Control of Sensor Networks in IoT Based on RPSO

In this paper, we proposed a novel method to solve the coverage control problem of sensor networks in the Internet of Things (IoT). The coverage control is an important index to evaluate the performance of network services. Ensuring the quality of network services, it is mainly to maximize the coverage of the network and minimize the energy consumption at the same time for the purpose of extending the network life cycle effect. Because of the overlay redundancy, it adopts the sleeping scheduling mechanism of nodes. The optimal solution is obtained after utilizing the coverage rate and the node sleep rate as the optimization objective function. Particle swarm optimization (PSO) is a group intelligent optimization algorithm. In practical applications, PSO often convergence in the local optimal solution prematurely. In order to balance the global search ability and convergence speed of PSO, We have improved the PSO based on the resampling technique, named resampled PSO (RPSO). The RPSO can not only maintain the diversity of the population, which can avoid premature convergence of the algorithm to some extent, but also ensure that each particle is active, reducing the calculation of redundancy, thereby improving the efficiency of the algorithm. The experimental results show that the RPSO can deal with complex multipeak optimization problem efficiently and reliably. Then the RPSO is used to solve the coverage control problem of sensor networks in IoT and has a great performance.

Identification of robust adaptation gene regulatory network parameters using an improved particle swarm optimization algorithm.

Robust adaptation is a critical ability of gene regulatory network (GRN) to survive in a fluctuating environment, which represents the system responding to an input stimulus rapidly and then returning to its pre-stimulus steady state timely. In this paper, the GRN is modeled using the Michaelis-Menten rate equations, which are highly nonlinear differential equations containing 12 undetermined parameters. The robust adaption is quantitatively described by two conflicting indices. To identify the parameter sets in order to confer the GRNs with robust adaptation is a multi-variable, multi-objective, and multi-peak optimization problem, which is difficult to acquire satisfactory solutions especially high-quality solutions. A new best-neighbor particle swarm optimization algorithm is proposed to implement this task. The proposed algorithm employs a Latin hypercube sampling method to generate the initial population. The particle crossover operation and elitist preservation strategy are also used in the proposed algorithm. The simulation results revealed that the proposed algorithm could identify multiple solutions in one time running. Moreover, it demonstrated a superior performance as compared to the previous methods in the sense of detecting more high-quality solutions within an acceptable time. The proposed methodology, owing to its universality and simplicity, is useful for providing the guidance to design GRN with superior robust adaptation.

Optimization Design of Trusses Based on Covariance Matrix Adaptation Evolution Strategy Algorithm

Covariance matrix adaptation evolution strategy algorithm (CMA-ES) is a newly evolution algorithm. It has become a powerful tool for solving highly nonlinear multi-peak optimization problems. In many real-world optimization problems, the location of multiple optima is often required in a search space. In order to evaluate the solution, thousands of fitness function evaluations are involved that is a time consuming or expensive processes. Therefore, conventional stochastic optimization methods meet a special challenge for a very large number of problem function evaluations. Aiming to overcome the shortcoming of stochastic optimization methods in the high calculation cost, a truss optimal method based on CMA-ES algorithm is proposed and applied to solve the section and shape optimization problems of trusses. The study results show that the method is feasible and has the advantages of high accuracy, high efficiency and easy implementation.

Load Distribution Calculation of Pile Group Using Artificial Bee Colony Algorithm

Artificial bee colony algorithm (ABC) is a newly swarm intelligence optimization algorithm. It has become a powerful tool for solving highly nonlinear multi-peak optimization problems. The results of performances testing using three benchmark functions show that the numbers of evaluation for fitness function of ABC are obviously less than that using particle swarm optimization algorithm. Thus, ABC has better suitability for solving multi-modal optimization problems. Finally, ABC algorithm is applied to the load distribution calculation of pile group. The result shows that the ABC is feasible and has the advantages of high efficiency and easy implementation

An improved quantum-behaved particle swarm optimization for multi-peak optimization problems

In this paper, we propose an improved quantum-behaved particle swarm optimization (QPSO), namely species-based QPSO (SQPSO), using the notion of species for solving multi-peak optimization problems. In the proposed SQPSO, the population is divided into subpopulations (species) based on their similarities. Each species is grouped around a dominating particle called the species seed. During the process of iterations, species are able to simultaneously optimize towards multiple optima by using QPSO, so each peak will definitely be searched in parallel, regardless of whether it is global or local optima. Further, SQPSO is applied to solve systems of nonlinear equations describing certain fitness functions, which are multi-peak functions. Our experiments demonstrate that SQPSO is able to search multiple peaks of a given function as accurate and efficient as possible. Finally the experiments for the solutions of systems of nonlinear equations show that the algorithm is successful in locating multiple solutions with better accuracy.

Quantum-behaved particle swarm optimization algorithm with random selection of optimal individual

The particles are easy to mass into a small search space in late stage and thus the diversity of swarms decline.Based on the analysis of quantum-behaved Particle Swarm Optimization(PSO) algorithm,a method of random selection of optimal individual was proposed to improve the diversity in searching progress.The of the improved QPSO was compared with the original PSO and the original QPSO using the testing function of the benchmark.Experimental results demonstrate that the improved QPSO is more suitable for resolving the multi-peak optimization problem.

局所的適応プランと大域的探索手法を融合した遺伝型適応プランシステムによる多峰性問題の最適化

局所的適応プランと大域的探索手法を融合した遺伝型適応プランシステムによる多峰性問題の最適化

Genetic algorithm optimization of multi-peak problems: studies in convergence and robustness

Engineering design studies can often be cast in terms of optimization problems. However, for such an approach to be worthwhile, designers must be content that the optimization techniques employed are fast, accurate and robust. This paper describes recent studies of convergence and robustness problems found when applying genetic algorithms (GAs) to the constrained, multi-peak optimization problems often found in design. It poses a two-dimensional test problem which exhibits a number of features designed to cause difficulties with standard GAs and other optimizers. The application of the GA to this problem is then posed as a further, essentially recursive problem, where the control parameters of the GA must be chosen to give good performance on the test problem over a number of optimization attempts. This overarching problem is dealt with both by the GA and also by the technique of simulated annealing. It is shown that, with the appropriate choice of control parameters, sophisticated niche forming techniques can significantly improve the speed and performance of the GA for the original problem when combined with the simple rejection strategy commonly employed for handling constraints. More importantly, however, it also shows that more sophisticated multi-pass, constraint penalty functions, culled from the literature of classical optimization theory, can render such methods redundant, yielding good performance with traditional GA methods.