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New metaheuristic algorithms have soared over the past ten years. A common practice in proposing a new algorithm is validating it on several benchmark functions and engineering problems. Although CEC benchmark problems are specifically designed to validate the performance of new meta-heuristics, engineering design problems from pressure vessels to springs have been used in hundreds of papers to prove algorithm efficiency. To date, no benchmark practices have been established yet, i.e., researchers design their own benchmark to validate their algorithm. Thus, the high number of new algorithms combined with the high number of different benchmarking setups complicates the comparing and validating processes. In this paper, we study benchmark practices related to engineering applications. In particular, our exhaustive qualitative systematic review focuses on metaheuristics applied to the tension/compression spring design problem (TCSDP). The aim of this study is threefold: (i) evaluate where the field stands in regards of algorithm performance on the TCSDP, (ii) evaluate benchmarking practices, and (iii) facilitate future algorithm comparison. For these purposes, we first review all the existing metaheuristics applied to the TCSDP in their first publication. For each paper, we gather the data regarding the problem definition, the simulation setup, and the optimized design. We evaluated the data through several metrics to find the best-optimized design so far. Our findings and analysis concluded that the field of metaheuristics and its benchmarking practice have not reached maturity yet. Thus, we recommend some actions to address the issues and provide future research directions. [ABSTRACT FROM AUTHOR]

Sine cosine algorithm (SCA) is a recently developed and widely used metaheuristic to perform global optimization tasks. Due to its simplicity in structure and reasonable performance, it has been utilized to solve several real-world applications. This paper proposes an alternate version of the SCA by adopting the greedy approach of search, crossover and exponentially decreased transition control parameter to overcome the issues of low exploitation, insufficient diversity and premature convergence. The proposed algorithm, called ECr-SCA, is validated and compared with the original SCA using computational time, diversity, performance index, statistical and convergence analysis on a set of 23 standard benchmark problems. Later, the proposed ECr-SCA is compared with seventeen other algorithms including improved versions of the SCA and state-of-the-art algorithms. Furthermore, the ECr-SCA is used to train multi-layer perceptron and the results are compared with variants of SCA and other metaheuristics. Overall comparison based on several different metrics illustrates the significant improvement in the search strategy of the SCA by the proposal of the ECr-SCA. • The enhanced sine cosine algorithm (ECr-SCA) is proposed for global optimization. • A greedy search and modified transition parameter are used to increase exploitation. • The multi-parent crossover is embedded to enhance diversity and global search. • The excellent performance of the ECr-SCA is validated on benchmark problems. • The ECr-SCA is also used to train multilayer perceptron. [ABSTRACT FROM AUTHOR]

This paper introduces a bio-inspired metaheuristic optimization algorithm named Tunicate Swarm Algorithm (TSA). The proposed algorithm imitates jet propulsion and swarm behaviors of tunicates during the navigation and foraging process. The performance of TSA is evaluated on seventy-four benchmark test problems employing sensitivity, convergence and scalability analysis along with ANOVA test. The efficacy of this algorithm is further compared with several well-regarded metaheuristic approaches based on the generated optimal solutions. In addition, we also executed the proposed algorithm on six constrained and one unconstrained engineering design problems to further verify its robustness. The simulation results demonstrate that TSA generates better optimal solutions in comparison to other competitive algorithms and is capable of solving real case studies having unknown search spaces. Note that the source codes of the proposed TSA algorithm are available at [ABSTRACT FROM AUTHOR]