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The scientific field of optimization has witnessed an increasing trend in the development of metaheuristic algorithms within the current decade. The vast majority of the proposed algorithms have been proclaimed as superior and highly efficient compared to their contemporary counterparts by their own developers, which should be verified on a set of benchmark cases if it is to give conducive insights into their true capabilities. This study completes a comprehensive investigation of the general optimization capabilities of the recently developed nature-inspired metaheuristic algorithms, which have not been thoroughly discussed in past literature studies due to their new emergence. To overcome this deficiency in the existing literature, optimization benchmark problems with different functional characteristics will be solved by some of the widely used recent optimizers. Unconstrained standard test functions comprised of thirty-four unimodal scalable optimization problems with varying dimensionalities have been solved by these competitive algorithms, and respective estimated solutions have been evaluated relying on the performance metrics defined by the statistical analysis of the predictive results. Convergence curves of the algorithms have been construed to observe the evolution trends of objective function values. To further delve into comprehensive analysis on unconstrained test cases, CEC 2013 problems have been considered for comparison tools since their resemblances of the following features of real-world complex algorithms. The optimization capabilities of eleven metaheuristics algorithms have been comparatively analyzed on twenty-eight multidimensional problems. Finally, fourteen complex engineering problems have been optimized by the algorithms to scrutinize their effectiveness on handling the imposed design constraints. [ABSTRACT FROM AUTHOR]

This theoretical research study proposes a novel Chaotic Quasi-Oppositional Arithmetic Optimization Algorithm (COAOA) for thermo-economic optimization of a shell and tube condenser working with refrigerant mixtures. Arithmetic Optimization Algorithm (AOA) is a recently emerged metaheuristic algorithm considering different mathematical operators to optimize the candidate solutions over a wide range of search domains. The effectiveness the COAOA is assessed by applying it to a set of benchmark optimization problems and comparing the obtained solutions with that of the original AOA and its quasi-oppositional variant. The COAOA has been employed to acquire the minimum value of the total annual cost of the shell and tube condenser by iteratively varying nine decision variables of mass flow rate, shell diameter, the tube inside diameter, tube length, number of tube passes, tube layout, tube pitch ratio, the total number of baffles, and diameter ratio. Three different case studies are solved using different refrigerant pairs used for in-tube flow to show the proposed metaheuristic optimizer's efficiency and effectivity on real-world mixed-integer optimization problem. Optimal results retrieved for different mixture pairs with varying mass fractions are compared with each other, and parametric configuration yielding the minimum total cost is decided. Finally, a comprehensive sensitivity analysis is performed to investigate the influences of the design variables over the considered problem objective. Overall analysis results indicate that COAOA can be an excellent optimizer to obtain a shell and tube condenser's optimal configuration within a reasonable computation time. [ABSTRACT FROM AUTHOR]