Search

In the last decade, the application of membrane-inspired evolutionary algorithms in real-life problems has attracted much attention due to their flexibility and parallelizability. Almost seven years have passed since the first membrane algorithms survey paper was published in 2014. Considering the importance and ongoing research on such algorithms and their applications in various disciplines, this paper presents a comprehensive review of the published literature and suggests future directions. This review aims to summarize and analyze membrane algorithms based on the used nature-inspired algorithm, membrane structure, membrane rules, and their merits and demerits. Furthermore, an extensive bibliography about their real-world applications is presented.

The benefits of tracking, identifying, measuring features of interest from structure responses have endless applications for saving cost, time and improving safety. To date, structural health monitoring (SHM) has been extensively applied in several fields, such as aerospace, automotive, and mechanical engineering. However, the focus of this paper is to provide a comprehensive up-to-date review of civil engineering structures such as buildings, bridges, and other infrastructures. For this reason, this article commences with a concise introduction to the fundamental definitions of SHM. The next section presents the general concepts and factors that determine the best strategy to be employed for SHM. Afterward, a thorough review of the most prevalent anomaly detection approaches, from classic techniques to advanced methods, is presented. Subsequently, some popular benchmarks, including laboratory specimens and real structures for validating the proposed methodologies, are demonstrated and discussed. Finally, the advantages and disadvantages of each method are summarized, which can be helpful in future studies.

Multi-objective truss optimisation is a research topic that has been less investigated in the literature compared to the single-objective cases. This paper investigates the comparative performance of fourteen new and established multi-objective metaheuristics when solving truss optimisation problems. The optimisers include multi-objective ant lion optimiser, multi-objective dragonfly algorithm, multi-objective grasshopper optimisation algorithm, multi-objective grey wolf optimiser, multi-objective multi-verse optimisation, multi-objective water cycle algorithm, multi-objective Salp swarm algorithm, success history-based adaptive multi-objective differential evolution, success history–based adaptive multi-objective differential evolution with whale optimisation, non-dominated sorting genetic algorithm II, hybridisation of real-code population-based incremental learning and differential evolution, differential evolution for multi-objective optimisation, multi-objective evolutionary algorithm based on decomposition, and unrestricted population size evolutionary multi-objective optimisation algorithm. The design problem is assigned to minimise structural mass and compliance subject to stress constraints. Eight classical trusses found in the literature are used for setting up the design test problems. Various optimisers are then implemented to tackle the problems. A comprehensive comparative study is given to critically analyse the performance of all algorithms in this problem area. The results provide new insights to the pros and cons of evolutionary multi-objective optimisation algorithms when addressing multiple, often conflicting objective in truss optimisation. The results and findings of this work assist with not only solving truss optimisation problem better but also designing customised algorithms for such problems.

This paper introduces a comprehensive overview of the Ant Lion Optimizer (ALO). ALO is a novel metaheuristic swarm-based approach introduced by Mirjalili in 2015 to emulate the hunting behavior of ant lions in nature life. The review is highlighted the applications that are utilized ALO algorithm to solve various optimization problems. In ALO, the best solution is determined to enhance the performance of the functional and efficient during the optimization process by finding the minimum or maximum values to solve a certain problem. Metaheuristic algorithms have become the focus of research due to introduce of decision-making and asses the benefits in solving various optimization problems. Also, a review of ALO variants is presented in this paper such as binary, modification, hybridization, enhanced, and others. The classifications of the ALO’s applications include the benchmark functions, machine learning applications, networks applications, engineering applications, software engineering, and Image processing. Finally, According to the reviewed papers published in the literature, the ALO algorithm is mostly utilized in solving various optimization problems. Presenting an overview and reviewing the ALO applications are the main aims of this review paper.

Solving practical mechanical problems is considered as a real challenge for evaluating the efficiency of newly developed algorithms. The present article introduces a comparative study on the application of ten recent meta-heuristic approaches to optimize the design of six mechanical engineering optimization problems. The algorithms are: the artificial bee colony (ABC), particle swarm optimization (PSO) algorithm, moth-flame optimization (MFO), ant lion optimizer (ALO), water cycle algorithm (WCA), evaporation rate WCA (ER-WCA), grey wolf optimizer (GWO), mine blast algorithm (MBA), whale optimization algorithm (WOA) and salp swarm algorithm (SSA). The performances of the algorithms are tested quantitatively and qualitatively using convergence speed, solution quality, and the robustness. The experimental results on the six mechanical problems demonstrate the efficiency and the ability of the algorithms used in this article. British Association for Psychopharmacology Benemérita Universidad Autónoma de Puebla

This erratum is published due to inconsistencies observed in referencing & citations on the final version.