Hybrid remora crayfish optimization for engineering and wireless sensor network coverage optimization

TL;DR: This paper proposes CSpL-SHADED, an improved algorithm for estimating complex solar photovoltaic model parameters, using dynamic crossover rate ranking and sub-population mechanisms, and parameter decomposition to enhance estimation accuracy and efficiency.

Abstract: Abstract Aiming at the issues of more difficult to solve and lower precision of six-axis robotic arm in inverse kinematics (IK) solution, a multi-strategy improved dung beetle optimization algorithm (ECDBO) is proposed. It improves performance in four aspects: population initiation, global search capability, search direction perturbation and jumping out of local optima. Sobol sequence strategy was introduced to initialize the dung beetle population, resulting in a more even distribution of individual dung beetles and increasing the diversity of initial population. Boundary optimization strategy is adopted to balance the requirements on search capability at different times. This approach enhances global search capability at the beginning and local search capability at the end of an iteration. Propose hybrid directional perturbation strategy to change the search direction of rolling dung beetles and stealing dung beetles. It allows for more detailed exploration and improves convergence accuracy. The Levy flight strategy is incorporated to perturb current optimal solution, enhancing algorithm’s ability to jump out of the local optimum. In order to verify performance of ECDBO algorithm, CEC2017 function tests and robotic arm IK solving experiments were conducted and compared with other algorithms. ECDBO ranked first on 21 functions in the 30 dimensions tested in CEC2017 and on 27 functions in the 100 dimensions. ECDBO performs well in the IK solving experiments of two robotic arms with better accuracy than other algorithms. The experimental results show that the ECDBO algorithm significantly improves the convergence and accuracy, and also performs excellently on the IK solving problem.

TL;DR: A framework is developed to explore the connection between effective optimization algorithms and the problems they are solving and a number of "no free lunch" (NFL) theorems are presented which establish that for any algorithm, any elevated performance over one class of problems is offset by performance over another class.

TL;DR: Optimization results prove that the WOA algorithm is very competitive compared to the state-of-art meta-heuristic algorithms as well as conventional methods.

TL;DR: This paper puts forward two useful methods for self-adaptation of the mutation distribution - the concepts of derandomization and cumulation and reveals local and global search properties of the evolution strategy with and without covariance matrix adaptation.

TL;DR: The qualitative and quantitative results prove the efficiency of SSA and MSSA and demonstrate the merits of the algorithms proposed in solving real-world problems with difficult and unknown search spaces.