Article
Computer Science, Artificial Intelligence
Abubakr Awad, George M. Coghill, Wei Pang
Summary: We proposed a novel Physarum-inspired competition algorithm (PCA) to solve discrete multi-objective optimization (DMOO) problems. Our algorithm is based on hexagonal cellular automata (CA) as a representation of problem search space and reaction-diffusion systems that control the Physarum motility. We have implemented a novel restart procedure to select the global Pareto frontier based on both personal experience and shared information. Extensive experimental and statistical analyses were conducted to assess the performance of our PCA against other evolutionary algorithms.
Article
Computer Science, Artificial Intelligence
Huseyin Hakli, Harun Uguz, Zeynep Ortacay
Summary: In recent years, many new nature-inspired optimization algorithms have been proposed and gained increasing popularity. These algorithms require less information, are reliable and robust, and are suitable for discrete problems. However, the abundance of algorithms makes it difficult to choose the correct one for a specific problem, and selecting the wrong algorithm can impact the solution quality. Therefore, studies comparing and evaluating algorithm performance are needed to guide practitioners and researchers.
Article
Computer Science, Artificial Intelligence
Chnoor M. Rahman, Tarik A. Rashid, Aram Mahmood Ahmed, Seyedali Mirjalili
Summary: In this work, a new multi-objective optimization algorithm called multi-objective learner performance-based behavior algorithm is proposed. The proposed algorithm is based on the process of moving graduated students from high school to college, and it produces a set of non-dominated solutions with better accuracy and diversity. Experimental results show that the algorithm outperforms other algorithms in terms of solution quality and processing time.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Marde Helbig, Andries Engelbrecht
Summary: This article extends the study on the application of partial dominance relation in another multi-objective optimization algorithm and evaluates its performance. The results provide further evidence that the partial dominance relation is an efficient approach to solve multi-objective optimization problems.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Chemistry, Multidisciplinary
Vinicius Renan de Carvalho, Ender Oezcan, Jaime Simao Sichman
Summary: This study investigates the performance of four state-of-the-art online hyper-heuristics with different characteristics in solving real-world multi-objective optimization problems. The results indicate that hyper-heuristics exhibit better cross-domain performance than single meta-heuristics, making them excellent candidates for solving new multi-objective optimization problems.
APPLIED SCIENCES-BASEL
(2021)
Article
Computer Science, Interdisciplinary Applications
S. Zhu, H. R. Maier, A. C. Zecchin
Summary: This paper investigates optimization methods for environmental problems and proposes 28 efficient feature metrics that can be applied to real-world problems to better understand their characteristics and determine the most suitable optimization algorithms.
ENVIRONMENTAL MODELLING & SOFTWARE
(2022)
Article
Automation & Control Systems
Saul Zapotecas-Martinez, Abel Garcia-Najera, Adriana Menchaca-Mendez
Summary: Traditionally, novel multi-objective optimization algorithms are evaluated on artificial test problems, which lack the properties of real-world applications. This paper presents a collection of multi-objective real-world problems from different disciplines to complement the evaluation of evolutionary algorithms. The study analyzes the conflict between objectives for each real-world problem and compares the performance of different multi-objective evolutionary algorithms.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Information Systems
Luis Ernesto Valencia-Segura, Miguel Gabriel Villarreal-Cervantes, Leonel German Corona-Ramirez, Francisco Cuenca-Jimenez, Jose Saul Munz-Reina
Summary: This paper presents an optimization approach using a one-degree-of-freedom spherical mechanism as a low-cost ankle rehabilitation device. The mechanism parameters are found by including the relative motion angle, the Grashof criterion, the force transmission, and the rehabilitation routine. Numerical simulation results and motion simulation of the CAD model validate the effectiveness of the obtained ankle rehabilitation mechanism.
Article
Computer Science, Artificial Intelligence
Souheila Khalfi, Amer Draa, Giovanni Iacca
Summary: A new compact algorithm, Compound Sinusoidal cDE (CScDE), is proposed in this paper, which outperforms seven state-of-the-art compact algorithms on various test beds and real-world optimization problems.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Environmental Sciences
Weiwei Bi, Minjie Chen, Shuwen Shen, Zhiyuan Huang, Jie Chen
Summary: This paper proposes a many-objective analysis framework for handling large real-world water distribution system design problems, which can comprehensively analyze and reveal the trade-offs among multiple objectives, thus facilitating the selection of the most appropriate design solutions.
Article
Computer Science, Artificial Intelligence
Bingda Tong, Lin Chen, Haibin Duan
Summary: This paper proposes an improved method of path planning and autonomous formation for unmanned aerial vehicles based on pigeon-inspired optimization and differential evolution. The mathematical model for UAV path planning is devised as a multi-objective optimization, and the method integrated by pigeon-inspired optimization and mutation strategies of differential evolution is developed to optimize feasible paths. Simulation results show the effectiveness of the proposed method compared with standard particle swarm optimization and standard differential evolution algorithms.
INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION
(2021)
Article
Computer Science, Artificial Intelligence
Benyamin Abdollahzadeh, Farhad Soleimanian Gharehchopogh, Seyedali Mirjalili
Summary: Metaheuristics play a crucial role in solving optimization problems, often inspired by the collective intelligence of natural organisms. This paper introduces a new metaheuristic algorithm, GTO, inspired by gorilla troops' social intelligence in nature. Results show that the GTO outperforms existing metaheuristics on most benchmark functions and engineering problems, especially in high-dimensional scenarios.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2021)
Article
Computer Science, Interdisciplinary Applications
Amol M. Dalavi, Alyssa Gomes, Aaliya Javed Husain
Summary: This paper statistically evaluates the impact and importance of nature-inspired optimization by analyzing works published between 2016 and 2020. The study finds that China, India, and the US are the highest contributors, and computer science, engineering, and mathematics are the top disciplines contributing to research. The top application areas include optimization, artificial intelligence, and decision sciences.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Siwakorn Anosri, Natee Panagant, Sujin Bureerat, Nantiwat Pholdee
Summary: A general approach based on the most probable point (MPP) method is developed for solving reliability truss optimisation, with the use of double loop optimisation to achieve consistent and accurate results. Newly developed algorithms show to outperform several state-of-the-art algorithms in efficiency and accuracy.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Jing Liu, Sreenatha Anavatti, Matthew Garratt, Hussein A. Abbass
Summary: A multi-operator continuous Ant Colony Optimisation (MACO(R)) algorithm is proposed in this paper, which selects suitable operators based on historical performance and population status to improve search accuracy. Experimental results demonstrate the superiority of the proposed algorithm on real-world problems and investigate the impacts of multi-operator framework and different operator combinations on algorithm performance.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
Guiliang Gong, Jiuqiang Tang, Dan Huang, Qiang Luo, Kaikai Zhu, Ningtao Peng
Summary: This paper proposes a flexible job shop scheduling problem with discrete operation sequence flexibility and designs an improved memetic algorithm to solve it. Experimental results show that the algorithm outperforms other algorithms in terms of performance. The proposed model and algorithm can help production managers obtain optimal scheduling schemes considering operations with or without sequence constraints.
SWARM AND EVOLUTIONARY COMPUTATION
(2024)
Article
Computer Science, Artificial Intelligence
Daniel Molina-Perez, Efren Mezura-Montes, Edgar Alfredo Portilla-Flores, Eduardo Vega-Alvarado, Barbara Calva-Yanez
Summary: This paper presents a new proposal based on two fundamental strategies to improve the performance of the differential evolution algorithm when solving MINLP problems. The proposal considers a set of good fitness-infeasible solutions to explore promising regions and introduces a composite trial vector generation method to enhance combinatorial exploration and convergence capacity.
SWARM AND EVOLUTIONARY COMPUTATION
(2024)
