Comparative Assessment Of Light-based Intelligent Search And Optimization Algorithms

Alataş, Bilal; Bingol, Harun

doi:10.33383/2019-029

Cited by 74 publications

(46 citation statements)

References 30 publications

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“…Another classification of meta-heuristic algorithms is presented in [59] where meta-heuristic algorithms are divided into nine different categories: swarm-based, chemical-based, biology-based, physics-based, sportsbased, musical-based, social-based, mathematical-based, and hybrid approaches. Besides the nine aforementioned categories, the authors in [60][61][62] added water-based, light-based and plant-based as three different classes of intelligent optimization algorithms.…”

Section: Literature Reviewmentioning

confidence: 99%

Single candidate optimizer: a novel optimization algorithm

et al. 2022

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Single-solution-based optimization algorithms have gained little to no attention by the research community, unlike population-based approaches. This paper proposes a novel optimization algorithm, called Single Candidate Optimizer (SCO), that relies only on a single candidate solution throughout the whole optimization process. The proposed algorithm implements a unique set of equations to effectively update the position of the candidate solution. To balance exploration and exploitation, SCO is integrated with the two-phase strategy where the candidate solution updates its position differently in each phase. The effectiveness of the proposed approach is validated by testing it on thirty three classical benchmarking functions and four real-world engineering problems. SCO is compared with three well-known optimization algorithms, i.e., Particle Swarm Optimization, Grey Wolf Optimizer, and Gravitational Search Algorithm and with four recent high-performance algorithms: Equilibrium Optimizer, Archimedes Optimization Algorithm, Mayfly Algorithm, and Salp Swarm Algorithm. According to Friedman and Wilcoxon rank-sum tests, SCO can significantly outperform all other algorithms for the majority of the investigated problems. The results achieved by SCO motivates the design and development of new single-solution-based optimization algorithms to further improve the performance. The source code of SCO is publicly available at: https://uk.mathworks.com/matlabcentral/fileexchange/116100-single-candidate-optimizer.

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Section: Literature Reviewmentioning

confidence: 99%

Single candidate optimizer: a novel optimization algorithm

et al. 2022

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“…Classical optimization and search algorithms are not effective for nonlinear, complex, dynamic large-scale problems with incomplete information [35]. Therefore, in some cases, the difficulties of design problems as well as the desire to find better solutions, combined with the inadequacy of existing meta-heuristic algorithms, lead researchers to develop new meta-heuristic algorithms.…”

Section: Chameleon Swarm Algorithmmentioning

confidence: 99%

Chameleon Swarm Algorithm Assisted Optimization of U-Slot Patch Antenna for Quad-Band Applications

Uluslu

2022

IEEE Access

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Existing optimization algorithms are insufficient in the face of problems with difficult measurement functions as well as a large number of design parameters. Therefore, to achieve such challenging applications, the Chameleon swarm algorithm and its variants, which belong to the family of meta-heuristic algorithms that have not been used in any antenna optimization study before, are used together with 3 different objective functions fitted from mathematical models. Then, a U-slot antenna application with 12 different variable design parameters with resonance frequencies of 3.5 GHz, 3.7 GHz, 5.2 GHz and 5.8 GHz is considered as a multidimensional and single-objective optimization problem. In this study, first of all, the success of the algorithm is reinforced by comparing the performance with other commonly used single and multi-objective optimization algorithms. In addition, the results obtained with different population parameters, weight coefficients, objective functions and variant models were compared. All these processes are compared within themselves, and the antenna results of the most successful result are displayed as 3D electromagnetic simulation. The results show that the optimization processes proposed for an antenna designer are a safe, practical and efficient solution for multidimensional and single-objective antenna optimization applications. In addition, any optimization problem with a large number of variable design parameters can undoubtedly be adapted to the Chameleon swarm algorithm.

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“…Yu et al [14] proposed a gray wolf localization algorithm based on the beetle search algorithm, which transformed the node localization problem into function-constrained optimization to prevent the grey wolf algorithm from falling into local optimization in later iterations. In [15], different ergodic chaotic systems were used for the first time to generate chaotic values instead of random values in optics inspired optimization (OIO) processes to enhance the global convergence speed and prevent stuck in local solutions of the classical OIO algorithm. Furthermore, a new application area for chaos was proposed.…”

Section: Introductionmentioning

confidence: 99%

Research on Multistrategy Improved Evolutionary Sparrow Search Algorithm and its Application

et al. 2022

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To address the problem of the sparrow search algorithm (SSA) has poor global search ability, weak local development ability, and easily falls into the local optimal solution, a multi-strategy improved evolutionary sparrow search algorithm (MSSA) is proposed. The introduction of the tent chaotic map improves the diversity of the initialization population, accelerates algorithm convergence, and improves convergence accuracy. Endow sparrow finders with a random search ability to coordinate the balance between global search and local development. To discover dangerous sparrow individuals, the mutation evolution operation is completed, and a greedy strategy is combined to improve the processing ability of the algorithm for local optimal solutions and make full use of each sparrow individual. Six benchmark functions were used to comprehensively verify the feasibility of the proposed algorithm based on four aspects: optimization ability, robustness, convergence ability, and optimization trajectory. These results indicate that the proposed algorithm is superior. Finally, through the comparison and analysis of the parameter identification and control strategies of the two servo systems in practical application, on the one hand, the advantages of the proposed algorithm in practical engineering applications are illustrated. In addition, a fuzzy PID control strategy based on MSSA is proposed. By adding step, sinusoidal, triangular wave and disturbance signals, simulation experiments show that the control strategy can significantly improve the dynamic and steady performance of the servo system.

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Comparative Assessment Of Light-based Intelligent Search And Optimization Algorithms

Cited by 74 publications

References 30 publications

Single candidate optimizer: a novel optimization algorithm

Single candidate optimizer: a novel optimization algorithm

Chameleon Swarm Algorithm Assisted Optimization of U-Slot Patch Antenna for Quad-Band Applications

Research on Multistrategy Improved Evolutionary Sparrow Search Algorithm and its Application

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