Article
Computer Science, Information Systems
Gianni D'Angelo, Francesco Palmieri
Summary: Genetic algorithms have shown effectiveness in solving real-world optimization problems, especially when combined with gradient-descent technique. The hybrid algorithm proposed in this work aims to improve the efficiency of GAs in finding global optimal solutions by utilizing the gradient-descent capability for local searching. Experimental results demonstrate competitiveness in solution precision and resource efficiency compared to other complex approaches.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Jakub Kudela
Summary: The article highlights the critical issue in evolutionary computation where some frequently used benchmark functions have their optima in the center of the feasible set, posing challenges in algorithm analysis. Through analyzing seven recently published methods, it was found that the presence of a center-bias operator enables easy identification of optima in the center of the benchmark set, rendering comparisons with methods lacking this bias meaningless. The computational performance comparison of these methods with established ones like 'differential evolution' and 'particle swarm optimization' showed varied results with only one new method consistently outperforming the old ones.
NATURE MACHINE INTELLIGENCE
(2022)
Article
Engineering, Chemical
Rajendran Shankar, Narayanan Ganesh, Robert Cep, Rama Chandran Narayanan, Subham Pal, Kanak Kalita
Summary: The optimization of industrial processes is crucial for profitability and sustainability. This paper proposes a hybrid metaheuristic algorithm, PSO-GSA, which combines the iterative improvement capability of PSO and GSA for selecting optimal process parameter levels. Comparisons on two real-world case studies show that the PSO-GSA algorithm outperforms traditional algorithms in finding significantly better solutions.
Article
Computer Science, Artificial Intelligence
Abdesslem Layeb
Summary: This article introduces a new population-based optimization algorithm called Tangent Search Algorithm (TSA) for solving optimization problems. The TSA utilizes a mathematical model based on the tangent function to move a given solution towards a better solution, balancing between exploitation and exploration search. It also incorporates a novel escape procedure to avoid local minima and an adaptive variable step-size for enhanced convergence capacity. Experimental results show that the TSA algorithm yields promising and competitive results in various tests, demonstrating its simplicity, efficiency, and requirement of only a small number of user-defined parameters.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Chemistry, Multidisciplinary
Grzegorz Kusztelak, Adam Lipowski, Jacek Kucharski
Summary: This paper presents a modified memetic genetic algorithm that introduces a cyclic symmetrization operator to improve exploitation by creating a more spherical distribution around the current leader. The algorithm is described, demonstrated, and theoretically analyzed. Its effectiveness is examined using a recognized benchmark of continuous functions test set on a multidimensional cube.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Artificial Intelligence
Giovanni Acampora, Angela Chiatto, Autilia Vitiello
Summary: This paper discusses the application of quantum computing in optimization problems and proposes the use of genetic algorithms as gradient-free methods to optimize the parameters of Quantum Approximate Optimization Algorithm (QAOA) circuit. Experimental results on noisy quantum devices solving MaxCut problem show that the genetic algorithm outperforms other gradient-free optimizers in terms of approximation ratio.
APPLIED SOFT COMPUTING
(2023)
Article
Mathematics
Mohammed H. Qais, Hany M. Hasanien, Rania A. Turky, Saad Alghuwainem, Marcos Tostado-Veliz, Francisco Jurado
Summary: This paper presents a novel metaheuristic optimization algorithm called the circle search algorithm (CSA) that is inspired by the geometrical features of circles. The CSA is evaluated against other algorithms through independent experiments using a variety of functions and engineering problems, and the results show that CSA outperforms other algorithms in terms of convergence speed and robustness to high-dimensional problems. Therefore, CSA is a promising algorithm for solving various optimization problems.
Article
Biotechnology & Applied Microbiology
Xiangyu Yin, Chrysanthos E. Gounaris
Summary: Crystal structure prediction (CSP) involves determining the most stable crystalline arrangements of materials based on their chemical compositions. CSP methodologies typically involve assessing material stability and conducting a search for exploring the design space. Developing an effective search algorithm is crucial, especially for inorganic crystals. Previous research on inorganic CSP has discussed empirical methods, guided-sampling algorithms, data-driven approaches, and a mathematical optimization-based search paradigm.
CURRENT OPINION IN CHEMICAL ENGINEERING
(2022)
Article
Multidisciplinary Sciences
Hueseyin Demirci, Niluefer Yurtay, Yueksel Yurtay, Esin Ayse Zaimoglu
Summary: In this study, a new metaheuristic algorithm called Electrical Search Algorithm (ESA) was proposed. ESA is based on the movement of electricity in high-resistive areas. It has a unique initialization scheme and utilizes unique exploration and exploitation strategies. ESA differs from other metaheuristics in terms of its initialization scheme, pole search mechanism, and update strategy of the best solutions. It was tested on benchmark functions and a clustering problem, and compared with other algorithms.
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING
(2023)
Article
Computer Science, Information Systems
Mohammad Noroozi, Hamed Mohammadi, Emad Efatinasab, Ali Lashgari, Mahdiyeh Eslami, Baseem Khan
Summary: The Golden Search Optimization Algorithm (GSO) is an effective population-based optimization algorithm that uses random solutions and a simple mathematical model to reach global optimum. The algorithm utilizes a transfer operator for adaptive step size adjustment to balance explorative and exploitative behavior in the search.
Article
Computer Science, Artificial Intelligence
Malik Braik, Alaa Sheta, Heba Al-Hiary
Summary: The study introduces a novel nature-inspired search optimization algorithm called Capuchin Search Algorithm (CapSA), which is designed based on the foraging behaviors of capuchin monkeys in forests to solve global optimization problems efficiently.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Engineering, Environmental
S. N. Poojitha, V Jothiprakash, Bellie Sivakumar
Summary: The study proposes a chaos-directed genetic algorithm (CDGA) for optimizing the design of water distribution networks (WDNs). By introducing two novel frameworks and exploring the influence of high-dimensionality chaotic systems, the CDGA models outperform traditional genetic algorithms (GA) and other optimization techniques in terms of search efficacy.
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
(2022)
Article
Computer Science, Artificial Intelligence
Antonio G. Spampinato, Rocco A. Scollo, Vincenzo Cutello, Mario Pavone
Summary: Community detection, an important research topic in Complex Network Analysis, plays a significant role in interpreting and understanding various systems in neuroscience, biology, social science, and economy. This paper introduces an immune optimization algorithm (opt-IA) for detecting community structures, aiming to maximize the modularity of the identified communities. Compared with 20 heuristics and metaheuristics, opt-IA demonstrates superior performance while being comparable to the Hyper-Heuristic method. The results confirm that opt-IA, despite relying on a purely random process, is reliable and efficient.
Article
Automation & Control Systems
Francesco Farina, Giuseppe Notarstefano
Summary: This article introduces a class of novel distributed algorithms for solving stochastic big-data convex optimization problems, involving consensus steps and updates on decision variables. It discusses the convergence of dynamic consensus algorithm and the algorithm's convergence to the optimal cost in expected value. The algorithm is tested on synthetic and real text data, showing promising results.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2021)
Article
Computer Science, Artificial Intelligence
Wei Zhou, Liang Feng, Kay Chen Tan, Min Jiang, Yong Liu
Summary: Dynamic multiobjective optimization problem refers to a multiobjective optimization problem that varies over time. To solve this kind of problem, evolutionary search with prediction approaches have been developed to estimate the changes in the problem. However, existing prediction methods only focus on the change in the decision space. In this article, a new approach is proposed that conducts prediction from both the decision and objective spaces. Experimental results show the effectiveness of the proposed method in solving both benchmark and real-world DMOPs.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2022)