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A novel optimization algorithm called hybrid salp swarm algorithm with teaching-learning based optimization (HSSATLBO) is proposed in this paper to solve reliability redundancy allocation problems (RRAP) with nonlinear resource constraints. Salp swarm algorithm (SSA) is one of the newest meta-heuristic algorithms which mimic the swarming behaviour...
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... • Wilcoxon signed-rank test. Wilcoxon signed-rank test is often employed for comparing the significant difference between two optimization algorithms (Bagheri Tolabi et al. 2021;Kumar and Singh 2021;Kundu et al. 2022;Li et al. 2022). Compared with the t-test, Wilcoxon signed-rank test has the following three advantages (Derrac et al. 2011): (1) Wilcoxon signed-rank test is more sensitive than the t-test; ...
Backtracking search algorithm (BSA) is a very popular and efficient population-based optimization technique. BSA has a very simple structure and good global search ability. However, BSA may be trapped into the local optimum in solving challenging multimodal optimization problems due to the single learning strategy. To enhance the global search ability of BSA, this paper proposes an improved version of BSA called backtracking search algorithm driven by generalized mean position (GMPBSA). In GMPBSA, two types of generalized mean positions are defined based on the built feature zones, which are employed to design the comprehensive learning mechanism consisting of three candidate learning strategies. Note that, this learning mechanism doesn’t introduce new control parameters and refer to the complex calculation. To verify the performance of GMPBSA, GMPBSA is used to solve the well-known CEC 2013 and CEC 2017 test suites, and three complex engineering optimization problems. Experimental results support the great potential of GMPBSA applied to the challenging multimodal optimization problems. The source code of GMPBSA can be found from https://github.com/jsuzyy/GMPBSA.
... Therefore, scholars commonly use many strategies to strengthen the SSA [62][63][64][65]. In recent years, SSA and its improved variants have manifested their capability in solving various optimization problems and many real-life applications [66][67][68][69][70][71][72][73][74] and exhibit outstanding performance in the domain of image segmentation [62,[75][76][77][78]. Nonetheless, these improved SSA variants still have some drawbacks of improper balance between exploitation and exploration phases and lack of population diversity. ...
If found and treated early, fast-growing skin cancers can dramatically prolong patients’ lives. Dermoscopy is a convenient and reliable tool during the fore-period detection stage of skin cancer, so the efficient processing of digital images of dermoscopy is particularly critical to improving the level of a skin cancer diagnosis. Notably, image segmentation is a part of image preprocessing and essential technical support in the process of image processing. In addition, multi-threshold image segmentation (MIS) technology is extensively used due to its straightforward and effective features. Many academics have coupled different meta-heuristic algorithms with MIS to raise image segmentation quality. Nonetheless, these meta-heuristic algorithms frequently enter local optima. Therefore, this paper suggests an improved salp swarm method (ILSSA) that combines iterative mapping (IM) and local escaping operator (LEO) to address this drawback. Besides, this paper also proposes the ILSSA-based MIS approach, which is triumphantly utilized to segment dermoscopic images of skin cancer. This method uses 2D Kapur's entropy as the objective function and employs non-local means (NLM) 2D histogram to represent the image information. Furthermore, an array of benchmark function test experiments demonstrated that ILSSA could alleviate the local optimal problem more effectively than other compared algorithms. Afterward, the skin cancer dermoscopy image segmentation experiment displayed that the proposed ILSSA-based MIS method obtained superior segmentation results than other MIS peers and was more adaptable at different thresholds.
... Newly enhanced algorithms include the fractional-order modified Harris hawks optimizer (FMHHO) 26 , the modified manta ray foraging optimization algorithm (MMRFOA) 27 , an enhanced slime mould algorithm 28 , the hybrid marine predator algorithm (HMPA) 29 , partitioned step particle swarm optimization (PSPSO) 30 , the improved chimp optimization algorithm (ICHOA) 31 , the high performance cuckoo search algorithm (HPCSA) 32 , the comprehensive learning marine predator algorithm (CLMPA) 33 , the enhanced sparrow search algorithm (ESSA) 34 , the hybrid algorithm that is known as three-learning strategy PSO (TLS-PSO) 35 , the enhanced shuffled shepherd optimization algorithm (ESSOA) 36 , the hybrid salp swarm algorithm with teaching-learning-based optimization (HSSATLBO) 37 , and an enhanced hybrid of crisscross optimization and the arithmetic optimization algorithm (CSOAOA) 38 . Both original and enhanced metaheuristic optimization algorithms are used in a wide range of fields, including engineering, business, transportation, energy, and even the social sciences. ...
The complexity of engineering optimization problems is increasing. Classical gradient-based optimization algorithms are a mathematical means of solving complex problems whose ability to do so is limited. Metaheuristics have become more popular than exact methods for solving optimization problems because of their simplicity and the robustness of the results that they yield. Recently, population-based bio-inspired algorithms have been demonstrated to perform favorably in solving a wide range of optimization problems. The jellyfish search optimizer (JSO) is one such bio-inspired metaheuristic algorithm, which is based on the food-finding behavior of jellyfish in the ocean. According to the literature, JSO outperforms many well-known meta-heuristics in a wide range of benchmark functions and real-world applications. JSO can also be used in conjunction with other artificial intelligence-related techniques. The success of JSO in solving diverse optimization problems motivates the present comprehensive discussion of the latest findings related to JSO. This paper reviews various issues associated with JSO, such as its inspiration, variants, and applications, and will provide the latest developments and research findings concerning JSO. The systematic review contributes to the development of modified versions and the hybridization of JSO to improve upon the original JSO and present variants, and will help researchers to develop superior metaheuristic optimization algorithms with recommendations of add-on intelligent agents.
... Most recently in 2022, Kundu et. al., used a hybrid salp swarm algorithm (HSS-TLBO) based on teachinglearning based optimization (TLBO) for RRAPs, aiming to merge the SSA ability of global search with the fast converging of (TLBO) to maximize reliability [16]. ...
... Problem (P1) symbolizes the series-parallel system depicted in Fig. 4; the problem is mathematically formulated as in Eq. (15). Table 2 shows the test input data [2], the (w5) value differs according to [16] as specified in Table 2, this is to be considered in the comparisons. For problem P2, the complex/bridge system is illustrated in Fig. 5, the formulation of the problem is shown in Eq. (16). ...
... Table 2 shows the test input data [2], the (w5) value differs according to [16] as specified in Table 2, this is to be considered in the comparisons. For problem P2, the complex/bridge system is illustrated in Fig. 5, the formulation of the problem is shown in Eq. (16). The input data used for the test are given in Table 3 [2]. ...
In this work, the wild horse optimization (WHO) algorithm, known for its ease of use, efficiency, and fast convergence, is explored in solving the reliability redundancy allocation problem (RRAP) for series-parallel systems. This problem has as of late caught the attention of researchers in this area, especially in today’s rapidly growing field of artificial intelligence. The NP-hard RRAP problem deals with maximizing of reliability under certain constraints. This work uses WHO algorithm to maximize the overall system reliability by determining how many redundant components are to be used along with their reliabilities in each subsystem, such reliability is constrained by cost, volume, and weight. Testing is carried out to show the effectiveness of this algorithm using four known numerical examples, results are to be compared with simplified swarm algorithm (SSO), attraction-repulsion imperialist competitive algorithm (AR-ICA), hybrid salp swarm algorithm and teaching-learning based optimization (HSSTLBO), particle swarm optimization (PSO), and gradient based optimization (GBO). Computational results show that WHO was able to find better feasible near-optimal solutions effectively and efficiently in terms of population size and number of iterations. © 2022. International Journal of Intelligent Engineering and Systems.All Rights Reserved
... Recently, Kundu et. al. [59] proposed a hybrid salp swarm algorithm with teaching-learning-based optimization (HSSATLBO) for solving RRAP. ...
The main objective of this paper is to present an improved neural network algorithm (INNA) for solving the reliability-redundancy allocation problem (RRAP) with nonlinear resource constraints. In this RRAP, both the component reliability and the redundancy allocation are to be considered simultaneously. Neural network algorithm (NNA) is one of the newest and efficient swarm optimization algorithms having a strong global search ability that is very adequate in solving different kinds of complex optimization problems. Despite its efficiency, NNA experiences poor exploitation, which causes slow convergence and also restricts its practical application of solving optimization problems. Considering this deficiency and to obtain a better balance between exploration and exploitation, searching procedure for NNA is reconstructed by implementing a new logarithmic spiral search operator and the searching strategy of the learner phase of teaching–learning-based optimization (TLBO) and an improved NNA has been developed in this paper. To demonstrate the performance of INNA, it is evaluated against seven well-known reliability optimization problems and finally compared with other existing meta-heuristics algorithms. Additionally, the INNA results are statistically investigated with the Wilcoxon sign-rank test and Multiple comparison test to show the significance of the results. Experimental results reveal that the proposed algorithm is highly competitive and performs better than previously developed algorithms in the literature.
The standard metaheuristics are commonly based on a single metaphorical model miming a particular animal group's food-searching behaviour. As a result, the contributions of such approaches are becoming inadequate in dealing with the evolving complexities of optimizing objective functions. Salp Swarm Algorithm (SSA) is one of them, a new swarm intelligence-based technique relying on a lone metaphor introduced for tackling global optimization issues. Nonetheless, SSA has garnered substantial acknowledgement and attraction among the research community because it is easy to implement and requires few control parameters to fine-tune. However, the typical SSA encounters confinement issues in local optima and an insufficient convergence pace when confronted with more intricate scenarios due to deficient population diversity, local exploitation, and global exploration. Therefore, this research integrates a Quantized Orthogonal Experimentation (QOX) operator to enhance population variety and intensify SSA's local exploitation and global explorative potential. The resulting hybrid approach is named QOX-SSA. QOX-SSA's optimization skill is demonstrated using 14 fundamental and 30 advanced benchmark problems of IEEE-CEC-2014 and comparing its effectiveness to some contemporary metaheuristics. Three nonparametric tests are conducted to verify the statistical importance of QOX-SSA's results. Furthermore, the applicability of QOX-SSA is examined by implementing it to train the Radial Basis Function Neural Network to classify data and resolve problems related to optimal feature selection. Experimental outcomes of QOX-SSA over different optimization issues confirm its superior performance compared to SSA and the alternate metaheuristics used for comparative analysis.
Compared to other techniques, particle swarm optimization is more frequently utilized because of its ease of use and low variability. However, it is complicated to find the best possible solution in the search space in large-scale optimization problems. Moreover, changing algorithm variables does not influence algorithm convergence much. The PSO algorithm can be combined with other algorithms. It can use their advantages and operators to solve this problem. Therefore, this paper proposes the onlooker multi-parent crossover discrete particle swarm optimization (OMPCDPSO). To improve the efficiency of the DPSO algorithm, we utilized multi-parent crossover on the best solutions. We performed an independent and intensive neighborhood search using the onlooker bees of the bee algorithm. The algorithm uses onlooker bees and crossover. They do local search (exploitation) and global search (exploration). Each of these searches is among the best solutions (employed bees). The proposed algorithm was tested on the allocation problem, which is an NP-hard optimization problem. Also, we used two types of simulated data. They were used to test the scalability and complexity of the better algorithm. Also, fourteen 2D test functions and thirteen 30D test functions were used. They also used twenty IEEE CEC2005 benchmark functions to test the efficiency of OMPCDPSO. Also, to test OMPCDPSO's performance, we compared it to four new binary optimization algorithms and three classic ones. The results show that the OMPCDPSO version had high capability. It performed better than other algorithms. The developed algorithm in this research (OMCDPSO) in 36 test functions out of 47 (76.60%) is better than other algorithms. The OMPCDPSO algorithm used many parts of the best solution. It put them in the multi-parent crossover and neighborhood search with onlookers. This made it better than DPSO. The Onlooker bees and multi-parent operators significantly impact the algorithm's performance.
This research paper develops a novel hybrid approach, called hybrid particle swarm optimization–teaching–learning-based optimization (hPSO-TLBO), by combining two metaheuristic algorithms to solve optimization problems. The main idea in hPSO-TLBO design is to integrate the exploitation ability of PSO with the exploration ability of TLBO. The meaning of “exploitation capabilities of PSO” is the ability of PSO to manage local search with the aim of obtaining possible better solutions near the obtained solutions and promising areas of the problem-solving space. Also, “exploration abilities of TLBO” means the ability of TLBO to manage the global search with the aim of preventing the algorithm from getting stuck in inappropriate local optima. hPSO-TLBO design methodology is such that in the first step, the teacher phase in TLBO is combined with the speed equation in PSO. Then, in the second step, the learning phase of TLBO is improved based on each student learning from a selected better student that has a better value for the objective function against the corresponding student. The algorithm is presented in detail, accompanied by a comprehensive mathematical model. A group of benchmarks is used to evaluate the effectiveness of hPSO-TLBO, covering various types such as unimodal, high-dimensional multimodal, and fixed-dimensional multimodal. In addition, CEC 2017 benchmark problems are also utilized for evaluation purposes. The optimization results clearly demonstrate that hPSO-TLBO performs remarkably well in addressing the benchmark functions. It exhibits a remarkable ability to explore and exploit the search space while maintaining a balanced approach throughout the optimization process. Furthermore, a comparative analysis is conducted to evaluate the performance of hPSO-TLBO against twelve widely recognized metaheuristic algorithms. The evaluation of the experimental findings illustrates that hPSO-TLBO consistently outperforms the competing algorithms across various benchmark functions, showcasing its superior performance. The successful deployment of hPSO-TLBO in addressing four engineering challenges highlights its effectiveness in tackling real-world applications.
At present, many improved algorithms have effectively improved the ability to converge to the optimal value. However, there are still difficulties: how to effectively balance the deep development ability of the algorithm in a small range and the extensive exploration ability in the global search field in the iterative process. For this reason, this topic first proposes a population state evaluation indicator, which continuously detects the state and diversity of the population with iteration. On this basis, the dual-strategy competition mechanism is adopted. Through the population state evaluation index, the strategy can select a strategy that is more conducive to rapid convergence according to the changes of the population during the iteration process. Through the advantage competition of the two strategies, the development ability of the algorithm has been steadily and persistently improved. However, there is still the risk of falling into the local optimal dilemma. Therefore, the change of population diversity is considered in the evaluation index of population status in this study. In order to further increase the activity of the global search range, the computational individuals used in the proposed environmental assessment index of population diversity are randomly selected. In general, in the greedy comparison of the two strategies, this study still ensures the consideration and influence of diversity, and effectively balances the ability of algorithm exploration and development.KeywordsHarmony Search AlgorithmDual-strategy contest mechanismPopulation state evaluationDiversity evaluationSystem reliability
