Symbiotic Organisms Search Algorithm: theory, recent advances and applications

The symbiotic organisms search algorithm is a very promising recent metaheuristic algorithm. It has received a plethora of attention from all areas of numerical optimization research, as well as engineering design practices. it has since undergone several modifications, either in the form of hybridization or as some other improved variants of the original algorithm. However, despite all the remarkable achievements and rapidly expanding body of literature regarding the symbiotic organisms search algorithm within its short appearance in the field of swarm intelligence optimization techniques, there has been no collective and comprehensive study on the success of the various implementations of this algorithm. As a way forward, this paper provides an overview of the research conducted on symbiotic organisms search algorithms from inception to the time of writing, in the form of details of various application scenarios with variants and hybrid implementations, and suggestions for future research directions

An Adaptive Fuzzy Symbiotic Organisms Search Algorithm and Its Applications

This paper discusses the development of a Symbiotic Organisms Search Algorithm (SOS) variant, called Adaptive Fuzzy SOS (FSOS). Like SOS, FSOS exploits three types of symbiosis operators namely mutualism, commensalism, and parasitism in order to undertake the search process. Unlike SOS, FSOS is able to adaptively select a single or any combination of mutualism, commensalism, and parasitism update operator(s) as the search progresses based on the current search status controlled by their individual probabilities via the fuzzy decision-making. To validate its performance, we have evaluated FSOS to solve 23 benchmark functions and take a t-way test generation as our case study. Experimental results demonstrate that FSOS exhibits competitive performance against its predecessor (SOS) and other competing metaheuristic algorithms

Adaptive Approaches Towards Better GA Performance in Dynamic Fitness Landscapes

We review different techniques for improving GA performance. By analysing the fitness landscape, a correlation measure between parents and offspring can be provided, and we can estimate effectively which genetic operator to use in the GA for a given fitness landscape. The response to selection equation further tells us how well the GA will do, and combining the two approaches gives us a powerful tool to automatically ensure the selection of the right parameter settings for a given problem. In dynamic environments the fitness landscape changes over time, and the evolved systems should be able to adapt to such changes. By introducing evolvable mutation rates and evolvable fitness formulae, we obtain such systems. The systems are shown to be able to adapt to both internal and external constraints and changes

Weight optimization of steel lattice transmission towers based on Differential Evolution and machine learning classification technique

Transmission towers are tall structures used to support overhead power lines. They play an important role in the electrical grids. There are several types of transmission towers in which lattice towers are the most common type. Designing steel lattice transmission towers is a challenging task for structural engineers due to a large number of members. Therefore, discovering effective ways to design lattice towers has attracted the interest of researchers. This paper presents a method that integrates Differential Evolution (DE), a powerful optimization algorithm, and a machine learning classification model to minimize the weight of steel lattice towers. A classification model based on the Adaptive Boosting algorithm is developed in order to eliminate unpromising candidates during the optimization process. A feature handling technique is also introduced to improve the model quality. An illustrated example of a 160-bar tower is conducted to demonstrate the efficiency of the proposed method. The results show that the application of the Adaptive Boosting model saves about 38% of the structural analyses. As a result, the proposed method is 1.5 times faster than the original DE algorithm. In comparison with other algorithms, the proposed method obtains the same optimal weight with the least number of structural analyses

A New Optimization Algorithm Based on Search and Rescue Operations

[EN] In this paper, a new optimization algorithm called the search and rescue optimization algorithm (SAR) is proposed for solving single-objective continuous optimization problems. SAR is inspired by the explorations carried out by humans during search and rescue operations. The performance of SAR was evaluated on fifty-five optimization functions including a set of classic benchmark functions and a set of modern CEC 2013 benchmark functions from the literature. The obtained results were compared with twelve optimization algorithms including well-known optimization algorithms, recent variants of GA, DE, CMA-ES, and PSO, and recent metaheuristic algorithms. The Wilcoxon signed-rank test was used for some of the comparisons, and the convergence behavior of SAR was investigated. The statistical results indicated SAR is highly competitive with the compared algorithms. Also, in order to evaluate the application of SAR on real-world optimization problems, it was applied to three engineering design problems, and the results revealed that SAR is able to find more accurate solutions with fewer function evaluations in comparison with the other existing algorithms. Thus, the proposed algorithm can be considered an efficient optimization method for real-world optimization problems.This study was partially supported by the Spanish Research Project (nos. TIN2016-80856-R and TIN2015-65515-C4-1-R).Shabani, A.; Asgarian, B.; Gharebaghi, SA.; Salido Gregorio, MA.; Giret Boggino, AS. (2019). A New Optimization Algorithm Based on Search and Rescue Operations. Mathematical Problems in Engineering. 2019:1-23. https://doi.org/10.1155/2019/2482543S1232019Bianchi, L., Dorigo, M., Gambardella, L. M., & Gutjahr, W. J. (2008). A survey on metaheuristics for stochastic combinatorial optimization. Natural Computing, 8(2), 239-287. doi:10.1007/s11047-008-9098-4Holland, J. H. 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A hybrid success history-based adaptive differential evolution and manta ray foraging optimization for multi-objective truss optimization problems

In this paper, a hybrid multi-objective metaheuristic algorithm based on the manta ray foraging optimization (MRFO) and the success history-based parameter adaptive differential evolution (SHADE) is developed to solve multi-objective truss optimization problems, called MO-SHADE-MRFO. SHADE is a variant of differential evolution with high performance in solving optimization problems, and MRFO is a novel metaheuristic algorithm inspired from the behavior of manta rays. In the proposed algorithm, the updating mechanism of MRFO is embedded into the SHADE, to enhance global convergence of SHADE for multi-objective truss optimization problems. The design problem is to minimize both structural mass and compliance subjected to stress constraints. Six benchmark truss optimization problems, including 10-bar, 25-bar, 37-bar, 120-bar, 200-bar and 942-bar trusses, are utilized to test the effectiveness of the proposed algorithm. The performance of the proposed algorithm is compared with nine state-of-the-art algorithms, in terms of metrics including hypervolume, inverted generational distance, and spacing-to-extent. The experiment results demonstrate that the proposed algorithm can obtain the best statistical values of metrics and the lowest standard deviation values in most test problems, which is more accurate than the compared algorithms. The Pareto solutions obtained by the proposed algorithm are well-distributed and smooth in each problem