Ala Kana, Imtiaz Ahmad^*

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3443-3464, 2023, DOI:10.32604/cmc.2023.040775

Abstract A newly proposed competent population-based optimization algorithm called RUN, which uses the principle of slope variations calculated by applying the Runge Kutta method as the key search mechanism, has gained wider interest in solving optimization problems. However, in high-dimensional problems, the search capabilities, convergence speed, and runtime of RUN deteriorate. This work aims at filling this gap by proposing an improved variant of the RUN algorithm called the Adaptive-RUN. Population size plays a vital role in both runtime efficiency and optimization effectiveness of metaheuristic algorithms. Unlike the original RUN where population size is fixed throughout the search process, Adaptive-RUN automatically… More >

Pavel Trojovský^*, Mohammad Dehghani

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1695-1730, 2023, DOI:10.32604/cmes.2023.028314

Abstract This paper introduces a new metaheuristic algorithm called Migration Algorithm (MA), which is helpful in solving optimization problems. The fundamental inspiration of MA is the process of human migration, which aims to improve job, educational, economic, and living conditions, and so on. The mathematical modeling of the proposed MA is presented in two phases to empower the proposed approach in exploration and exploitation during the search process. In the exploration phase, the algorithm population is updated based on the simulation of choosing the migration destination among the available options. In the exploitation phase, the algorithm population is updated based on… More >

Musaed Alrashidi^*

Computer Systems Science and Engineering, Vol.47, No.1, pp. 491-513, 2023, DOI:10.32604/csse.2023.038628

Abstract Statistical distributions are used to model wind speed, and the two-parameters Weibull distribution has proven its effectiveness at characterizing wind speed. Accurate estimation of Weibull parameters, the scale (c) and shape (k), is crucial in describing the actual wind speed data and evaluating the wind energy potential. Therefore, this study compares the most common conventional numerical (CN) estimation methods and the recent intelligent optimization algorithms (IOA) to show how precise estimation of c and k affects the wind energy resource assessments. In addition, this study conducts technical and economic feasibility studies for five sites in the northern part of Saudi… More >

Manfred Groll

Frontiers in Heat and Mass Transfer, Vol.15, pp. 1-7, 2020, DOI:10.5098/hmt.15.2

Abstract Each individual human being, groups of individuals, whole nations depend on the availability of energy for their survival. Without energy, no civilization can develop and sustain. In our globalised civilization, hundreds of millions of people cannot satisfy their needs for energy, be it in the elementary form of food, (clean) water for drinking, cooking and irrigation, (clean) air and (clean) soil for production of crops, or be it in energy required for heating/refrigeration, light, radio, TV, etc. Modern industrialized societies with their huge energy demand for industry, the transportation sector and for building up or maintaining elevated living standards for… More >

Pavel Trojovský^1,*, Mohammad Dehghani¹, Eva Trojovská¹, Eva Milkova²

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.2, pp. 1527-1573, 2023, DOI:10.32604/cmes.2023.025908

Abstract In this paper, based on the concept of the NFL theorem, that there is no unique algorithm that has the best performance for all optimization problems, a new human-based metaheuristic algorithm called Language Education Optimization (LEO) is introduced, which is used to solve optimization problems. LEO is inspired by the foreign language education process in which a language teacher trains the students of language schools in the desired language skills and rules. LEO is mathematically modeled in three phases: (i) students selecting their teacher, (ii) students learning from each other, and (iii) individual practice, considering exploration in local search and… More > Graphic Abstract

Hadi Givi^1,*, Marie Hubálovská²

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5283-5300, 2023, DOI:10.32604/cmc.2023.034695

Abstract Metaheuristic algorithms are one of the most widely used stochastic approaches in solving optimization problems. In this paper, a new metaheuristic algorithm entitled Billiards Optimization Algorithm (BOA) is proposed and designed to be used in optimization applications. The fundamental inspiration in BOA design is the behavior of the players and the rules of the billiards game. Various steps of BOA are described and then its mathematical model is thoroughly explained. The efficiency of BOA in dealing with optimization problems is evaluated through optimizing twenty-three standard benchmark functions of different types including unimodal, high-dimensional multimodal, and fixed-dimensional multimodal functions. In order… More >

El-Sayed M. El-kenawy^1,2, Abdelaziz A. Abdelhamid^3,4, Abdelhameed Ibrahim⁵, Seyedali Mirjalili^6,7, Nima Khodadad⁸, Mona A. Al duailij⁹, Amel Ali Alhussan^9,*, Doaa Sami Khafaga⁹

Computer Systems Science and Engineering, Vol.45, No.2, pp. 1917-1934, 2023, DOI:10.32604/csse.2023.032497

Abstract Metaheuristic optimization algorithms present an effective method for solving several optimization problems from various types of applications and fields. Several metaheuristics and evolutionary optimization algorithms have been emerged recently in the literature and gained widespread attention, such as particle swarm optimization (PSO), whale optimization algorithm (WOA), grey wolf optimization algorithm (GWO), genetic algorithm (GA), and gravitational search algorithm (GSA). According to the literature, no one metaheuristic optimization algorithm can handle all present optimization problems. Hence novel optimization methodologies are still needed. The Al-Biruni earth radius (BER) search optimization algorithm is proposed in this paper. The proposed algorithm was motivated by… More >

Hadi Givi¹, Marie Hubalovska^2,*

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 179-202, 2023, DOI:10.32604/cmc.2023.030379

Abstract Metaheuristic algorithms are widely used in solving optimization problems. In this paper, a new metaheuristic algorithm called Skill Optimization Algorithm (SOA) is proposed to solve optimization problems. The fundamental inspiration in designing SOA is human efforts to acquire and improve skills. Various stages of SOA are mathematically modeled in two phases, including: (i) exploration, skill acquisition from experts and (ii) exploitation, skill improvement based on practice and individual effort. The efficiency of SOA in optimization applications is analyzed through testing this algorithm on a set of twenty-three standard benchmark functions of a variety of unimodal, high-dimensional multimodal, and fixed-dimensional multimodal… More >

Qasem M. Kharma^*, Abdelrahman H. Hussein, Faris M. Taweel, Mosleh M. Abualhaj, Qusai Y. Shambour

Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 869-883, 2022, DOI:10.32604/iasc.2022.020415

Abstract The widespread and desirable features of IP and IEEE 802.11 networks have made these technologies a suitable medium for carrying voice over IP (VoIP). However, a bandwidth (BW) exploitation obstacle emerges when 802.11 networks are used to carry VoIP traffic. This BW exploitation obstacle is caused by the large 80-byte preamble size of the VoIP packet and a waiting time of 765 μs for each layer 2 VoIP frame. As a solution, IEEE 802.11n was consequently designed with a built-in layer 2 frame aggregation feature, but the adverse impact on the VoIP performance still needed to be addressed. Subsequent VoIP… More >

Peizhong Liu¹, Xiaofang Liu¹, Yanming Luo², Yongzhao Du¹, Yulin Fan¹, Hsuan‐Ming Feng³

Intelligent Automation & Soft Computing, Vol.25, No.2, pp. 385-394, 2019, DOI:10.31209/2019.100000100

Abstract Aiming at the drawback of artificial bee colony algorithm (ABC) with slow convergence speed and weak exploitation capacity, an enhanced exploitation artificial bee colony algorithm is proposed, EeABC for short. Firstly, a generalized opposition-based learning strategy (GOBL) is employed when initial population is produced for obtaining an evenly distributed population. Subsequently, inspired by the differential evolution (DE), two new search equations are proposed, where the one is guided by the best individuals in the next generation to strengthen exploitation and the other is to avoid premature convergence. Meanwhile, the distinction between the employed bee and the onlooker bee is not… More >