Non-inertial opposition-based particle swarm optimization and its theoretical analysis for deep learning applications

Kang, Ling; Chen, Ruey‐Shun; Cao, Wenliang; Chen, Yeh-Cheng

doi:10.1016/j.asoc.2019.106038

Cited by 26 publications

(4 citation statements)

References 36 publications

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“…The searching abilities of the metaheuristic optimization algorithms are improved using the different opposition based learning methods as the opposite candidate solutions have a higher probability of being nearer to the optimal solution than the randomly generated solutions and are more beneficial for generating new solutions in the search phases (Rahnamayan et al, 2008b;Roy et al, 2014;Ventresca et al, 2010). Different opposition methods are proposed and integrated with the algorithms to enhance their search abilities (El-Abd, 2011;Han & He, 2007;Kang et al, 2020;Kumar, Mandal, & Chakraborty, 2020;Rahnamayan et al, 2008a). In the SABES algorithm, the dynamic-opposite learning method is used at the initialization and search phase to enhance the performance of the algorithm to avoid the problem of local optimal stagnation and premature convergence.…”

Section: Dynamic-opposite Learning Based Improvement In Sabesmentioning

confidence: 99%

A Self‐adaptive Bald Eagle Search optimization algorithm with dynamic opposition‐based learning for global optimization problems

2022

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Bald Eagle Search optimization (BES) is introduced recently, which mimics the bald eagles' hunting and food searching behaviour. The capability of a BES algorithm is enhanced in this paper by avoiding local optima stagnation and premature convergence problems. The BES algorithm is modified to enhance the performance of the algorithm and the modified algorithm is called the Self Adaptive Bald Eagle Search (SABES) algorithm. Dynamic‐opposite Learning (DOL) method is invoked in the initialization phase to increase the population diversity and convergence speed. To find better global solutions, the exploitation capability of the SBES algorithm is enhanced by considering the dynamic‐opposite solutions. In addition, the algorithmic parameter values of the BES algorithm have been determined using the linear and non‐linear time‐varying adaption strategy to create a balance between the search abilities which promotes the overall performance of the algorithm. The performance of the SABES algorithm is validated by comparing the results of 50 benchmark functions and CEC2017 functions with different erstwhile algorithms. The proposed algorithm achieves the best results in 80% of the benchmark functions, whereas the BES only gets the best results in 56% of functions. For the CEC2017, the SABES algorithm achieves optimal results for 20 functions which are highest in comparison to state‐of‐the‐art algorithms. The feasibility and effectiveness of the proposed algorithm are checked using the 15 CEC2020 competition real‐world single‐objective constrained optimization problems. The SABES achieves a 100% success rate and 100% feasibility rate in comparison to other well‐regarded algorithms. The statistical significance of the algorithm has been proved using Friedman's mean rank and Wilcoxon sign rank test.

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Section: Dynamic-opposite Learning Based Improvement In Sabesmentioning

confidence: 99%

A Self‐adaptive Bald Eagle Search optimization algorithm with dynamic opposition‐based learning for global optimization problems

2022

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“…One set was randomly generated, and the other set was generated in the opposite direction based on the existing position. Literature [23] used the opposite direction for learning strategies. e reason for the opposite direction is because in a given environment, our search direction may be opposite to the direction of the optimal solution.…”

Section: Opposite Directionmentioning

confidence: 99%

A Novel Multiobjective Particle Swarm Optimization Combining Hypercube and Distance

Shu

Liu

Zhang

et al. 2022

Scientific Programming

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The multiobjective optimization problems are a common problem in various fields in the real society. Therefore, solving the multiobjective optimization problems are one of the important problems studied by many researchers in recent years. From the research in recent years, it can be seen that there is still a lot of room for development of particle swarm optimization in solving multiobjective optimization problems. This paper proposes a novel multiobjective particle swarm optimization combining hypercube and distance, called HDMOPSO. The particle velocity update part in this paper uses a combination of hypercube and distance. In order to prevent the algorithm from falling into the local optimum, the part also uses the nonlinear decreasing opposite mutation strategy, which enables the particles to explore a more area. Finally, a control strategy is used for external archive to improve the convergence and diversity of the algorithm. The algorithm has been simulated in 22 test problems and compared with multiobjective particle swarm optimization algorithms (MOPSOs) and multiobjective evolutionary algorithms (MOEAs). The results show that the HDMOPSO can effectively improve the convergence and diversity, so it is an effective improvement.

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“…OBL takes simultaneously advantage of a current estimate solution and its opposite point to improve the search capacity of the included algorithm. OBL concept has been incorporated into many metaheuristics such as Particle Swarm Optimization [20], Harmony Search [21] and Whale Optimization Algorithm [22]. SSA, JAYA and OBL methods have been coupled with each other in the literature before to solve various optimization problems [23,24], however, to the best knowledge of the authors, these three techniques have never been incorporated before.…”

Section: Introductionmentioning

confidence: 99%

An oppositional Salp Swarm: Jaya algorithm for thermal design optimization of an Organic Rankine Cycle

Turgut

2021

SN Appl. Sci.

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This study proposes a hybrid metaheuristic algorithm to tackle both single and multi objective optimization problems that are subjected to hard constraints. Twenty-four single objective optimization benchmark problems comprising unimodal and multi modal test functions have been solved by the proposed hybrid algorithm (OPSSAJ) and numerical results have been compared with those acquired by some of the new emerged metaheuristic optimizers. The proposed OPSSAJ shows a significant accuracy and robustness in most of the cases and proves its efficiency in solving high dimensional problems. As a real-world case study, seventeen operational design parameters of an organic rankine cycle (ORC) operating with a binary mixture of R227EA and R600 refrigerants are optimized by the proposed hybrid OPSSAJ to obtain the optimum values of contradicting dual objectives of second law efficiency and Specific Investment Cost. A Pareto curve composed of non-dominated solutions is constructed through the weighted sum method and the final solution is chosen by the reputed TOPSIS decision-maker. The pareto curve and best-compromising result obtained by utilizing the OPPSAJ are compared with that of acquired by using nondominated sorting genetic algorithm II (NSGA-II) and multiple objective particle swarm optimization (MOPSO) algorithms. The multi-objective ORC design obtained with the OPSSAJ yields a significant improvement in thermal efficiency and cost values compared to designs found by the NSGA-II and MOPSO algorithms. Furthermore, a sensitivity analysis is performed to observe the influences of the selected design variables on problem objectives.

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Non-inertial opposition-based particle swarm optimization and its theoretical analysis for deep learning applications

Cited by 26 publications

References 36 publications

A Self‐adaptive Bald Eagle Search optimization algorithm with dynamic opposition‐based learning for global optimization problems

A Self‐adaptive Bald Eagle Search optimization algorithm with dynamic opposition‐based learning for global optimization problems

A Novel Multiobjective Particle Swarm Optimization Combining Hypercube and Distance

An oppositional Salp Swarm: Jaya algorithm for thermal design optimization of an Organic Rankine Cycle

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