Titlebook: Handbook of Formal Optimization; Anand J. Kulkarni,Amir H. Gandomi Reference work 2024 Springer Nature Singapore Pte Ltd. 2024 Engineering

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Incorporating Nelder-Mead Simplex as an Accelerating Operator to Improve the Performance of Metaheurre commonly used for solving complex optimization problems. NMS is a search method that forms a simplex of points, iteratively transforming it to find the optimal solution. The incorporation of NMS in metaheuristic algorithms can significantly enhance the convergence speed and solution quality. The

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A Discrete Cuckoo Search Algorithm for the Cumulative Capacitated Vehicle Routing Probleme population properties of the cuckoo search algorithm, and its ability to progressively improve the solutions’ quality, with the strong effectiveness of greedy and heuristic algorithms. Unlike the original CS which was designed for solving continuous optimization problems, this implementation adapt

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Commonly Used Static and Dynamic Single-Objective Optimization Benchmark Problems to use a set of benchmark problems with different characteristics in order to evaluate the performance of algorithms designed for optimization problems. This allows researchers to examine how algorithms perform in different situations and what their strengths and weaknesses are. This chapter review

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Steganography Based on Fuzzy Edge Detection, Cohort Intelligence, and Thresholdingetect with the human eye. With the advent of digital media, it is imperative that robust ways to embed information are developed making it difficult for attackers to intercept. In this paper, a combination of Fuzzy Edge Detection and Cohort Intelligence (CI) is used to make the process of image hidi

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Multi-population Evolutionary and Swarm Intelligence Dynamic Optimization Algorithms: A Surveyptimization problems. In a dynamic optimization problem, the search space is affected by environmental changes over time. In multi-population evolutionary and swarm intelligence dynamic optimization algorithms, the number of sub-populations is a parameter determined either by the user or adaptively.

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Salp Swarm Algorithm for Optimization of Shallow Foundationsrm Algorithm (SSA) and the Improved Salp Swarm Algorithm (ISSA). The efficiency of each algorithm for designing shallow foundations is examined through numerical examples of benchmark foundations subjected to uniaxial loads, axial and flexural loads, and axial and flexural loads with dynamic column