Abhishek Kumar
ABSTRACT
Determination of the global optimum of complex non-convex optimization problems of the real-world applications has remained a challenging task. Many researchers have been developing various types of effective direct search-based methods to tackle these problems. In this paper, we introduce a new variant of the recently developed Spherical Search (SS) algorithm, which contains a powerful and effective self-adaptation structure to enhance the performance. To analyze the performance, proposed algorithm is tested on the 57 test problems collected from different real-world applications. The obtained results statistically confirm the efficacy and efficiency of the proposed algorithm.
- Piya Chootinan and Anthony Chen. 2006. Constraint handling in genetic algorithms using a gradient-based repair method. Computers & operations research 33, 8 (2006), 2263--2281.Google Scholar
- Abhishek Kumar, Rakesh Kumar Misra, Devender Singh, Sujeet Mishra, and Swagatam Das. 2019. The spherical search algorithm for bound-constrained global optimization problems. Applied Soft Computing (2019), 105734. Google ScholarCross Ref
- Abhishek Kumar, Guohua Wu, Mostafa Z. Ali, Rammohan Mallipeddi, Ponnuthurai Nagaratnam Suganthan, and Swagatam Das. 2020. A Test-suite of Non-Convex Constrained Optimization Problems from the Real-World and Some Baseline Results. Swarm and Evolutionary Computation, (2020). Google ScholarCross Ref
- Tetsuyuki Takahama and Setsuko Sakai. 2006. Constrained optimization by the ∈ constrained differential evolution with gradient-based mutation and feasible elites. In 2006 IEEE International Conference on Evolutionary Computation. IEEE, 1--8.Google ScholarCross Ref
Index Terms
- A self-adaptive spherical search algorithm for real-world constrained optimization problems
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