Multi-model Function Research Articles

A Modified Emperor Penguin Algorithm for Solving Stagnation in Multi-Model Functions

A Modified Emperor Penguin Algorithm for Solving Stagnation in Multi-Model Functions

Mother optimization algorithm: a new human-based metaheuristic approach for solving engineering optimization

This article’s innovation and novelty are introducing a new metaheuristic method called mother optimization algorithm (MOA) that mimics the human interaction between a mother and her children. The real inspiration of MOA is to simulate the mother’s care of children in three phases education, advice, and upbringing. The mathematical model of MOA used in the search process and exploration is presented. The performance of MOA is assessed on a set of 52 benchmark functions, including unimodal and high-dimensional multimodal functions, fixed-dimensional multimodal functions, and the CEC 2017 test suite. The findings of optimizing unimodal functions indicate MOA’s high ability in local search and exploitation. The findings of optimization of high-dimensional multimodal functions indicate the high ability of MOA in global search and exploration. The findings of optimization of fixed-dimension multi-model functions and the CEC 2017 test suite show that MOA with a high ability to balance exploration and exploitation effectively supports the search process and can generate appropriate solutions for optimization problems. The outcomes quality obtained from MOA has been compared with the performance of 12 often-used metaheuristic algorithms. Upon analysis and comparison of the simulation results, it was found that the proposed MOA outperforms competing algorithms with superior and significantly more competitive performance. Precisely, the proposed MOA delivers better results in most objective functions. Furthermore, the application of MOA on four engineering design problems demonstrates the efficacy of the proposed approach in solving real-world optimization problems. The findings of the statistical analysis from the Wilcoxon signed-rank test show that MOA has a significant statistical superiority compared to the twelve well-known metaheuristic algorithms in managing the optimization problems studied in this paper.

A Compact Pigeon-Inspired Optimization for Maximum Short-Term Generation Mode in Cascade Hydroelectric Power Station

Pigeon-inspired optimization (PIO) is a new type of intelligent algorithm. It is proposed that the algorithm simulates the movement of pigeons going home. In this paper, a new pigeon herding algorithm called compact pigeon-inspired optimization (CPIO) is proposed. The challenging task for multiple algorithms is not only combining operations, but also constraining existing devices. The proposed algorithm aims to solve complex scientific and industrial problems with many data packets, including the use of classical optimization problems and the ability to find optimal solutions in many solution spaces with limited hardware resources. A real-valued prototype vector performs probability and statistical calculations, and then generates optimal candidate solutions for CPIO optimization algorithms. The CPIO algorithm was used to evaluate a variety of continuous multi-model functions and the largest model of hydropower short-term generation. The experimental results show that the proposed algorithm is a more effective way to produce competitive results in the case of limited memory devices.

Multi-model function optimization based on immune clonal optimization with self-adaptive population size

In order to balance the impact of population size on the efficiency and effectiveness of the search algorithm,an immune clonal algorithm with self-adaptive population size for multi-modal function optimization problem was proposed.The change rules,magnitude and the detailed realization of population size were given.The change of self-adaptive population size with the evolutionary process was achieved.In addition,in terms of multi-modal function optimization characteristics,Baldwin learning was used as a local search strategy to enhance the search ability for the optimal solution.The experimental results show that the algorithm has better optimization ability,faster convergence and is more stable than existing algorithms.

A novel non-Lyapunov approach through artificial bee colony algorithm for detecting unstable periodic orbits with high orders

In this paper, a novel non-Lyapunov way is proposed to detect the unstable periodic orbits (UPOs) with high orders by a new artificial bee colony algorithm (ABC). And UPOs with high orders of nonlinear systems, are one of the most challenging problems of nonlinear science in both numerical computations and experimental measures. The proposed method maintains an effective searching mechanism with fine equilibrium between exploitation and exploration. To improve the performance for the optimums of the multi-model functions and to avoid the coincidences among the UPOs with different orders, we add the techniques as function stretching, deflecting and repulsion to ABC. The problems of detecting the UPOs are converted into a non-negative functions’ minimization through a proper translation, which finds a UPO such that the objective function is minimized. Experiments to different high orders UPOs of 5 wellknown and widely used nonlinear maps indicate that the proposed algorithm is robust, by comparison of results through the ABC and quantum-behaved particle swarm optimization (QPSO), respectively. And it is effective even in cases where the Newton-family algorithms may not be applicable. Density of the orbits are discussed. Simulation results show that ABC is superior to QPSO, and it is a successful method in detecting the UPOs, with the advantages of fast convergence, high precision and robustness.

Analogue filter design using differential evolution

Although analogue circuits have been successfully used in a variety of real life applications, the design of such circuits is very challenging and a time-consuming task. Automating the design of analogue circuit using optimisation algorithms is urgently needed. Algorithms inspired from natural evolution called evolutionary algorithms (EAs) were successfully used to solve variety of engineering problem. EAs are capable of providing pioneer solutions because they are capable of handling multimodel function, deal with functional non-linearity and measurement noise. In this paper, we explore the advantages of using differential evolution (DE) algorithm to select the optimal elements of analogue electronic circuit and specially passive and active filters. The produced results show that DE can find the optimal values of the proposed circuit elements such as resistor, capacitor and inductor in an efficient way. The developed circuits were tested with significant results.