On-site workshop investment problem: A novel mathematical approach and solution procedure by Nima Moradi (19418821)

A High‐Performance MPPT Solution for Solar DC Microgrids: Leveraging the Hippopotamus Algorithm for Greater Efficiency and Stability by Debabrata Mazumdar (18560506)

Logistics Optimization Using Hybrid Genetic Algorithm (HGA): A Solution to the Vehicle Routing Problem With Time Windows (VRPTW) by Ayesha Maroof (17984053)

“…This research introduces a cutting-edge Hybrid Genetic Algorithm-Solomon Insertion Heuristic (HGA-SIH) solution, reinforced by the powerful Solomon Insertion constructive heuristic to solve the VRPTW as an NP-hard problem. …”

Evolution Of Activation Functions for Neural Architecture Search by Nader, Andrew

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Predicting Android Malware Using Evolution Networks by Chahine, Joy

“…This issue requires the development of efficient solutions in order to keep up with the continuous evolution of malware. …”
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Salp swarm algorithm: survey, analysis, and new applications by Abualigah, Laith

“…The behavior of the species when traveling and foraging in the waters is the main source of SSA and MSSA. These two algorithms are put to test on a variety of mathematical optimization functions to see how they behave when it comes to finding the best solutions to optimization problems. …”
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Hybrid Cooperative Co-evolution for Large Scale Optimization by El-Abd, Mohammed

“…In this paper, we propose the idea of hybrid cooperative co-evolution (hCC). In CC, multiple instances of the same evolutionary algorithm work in parallel, each optimizes a different subset of the problem in hand. …”
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Hybrid Cooperative Co-evolution for the CEC15 Benchmarks by El-Abd, Mohammed

“…In its second stage, the method adopts different algorithms within the cooperative co-evolution (CC) framework to simultaneously optimize the generated groups. …”
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Digital circuit design through simulated evolution (SimE) by Sait, Sadiq M.

“…In this paper, the use of simulated evolution (SimE) algorithm in the design of digital logic circuits is proposed. …”
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Evaluating Parallel Simulated Evolution Strategies for VLSI Cell Placement by Sait, Sadiq M.

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Performance Assessment of Foraging Algorithms vs. Evolutionary Algorithms by El-Abd, Mohammed

“…This work provides a complete performance assessment of the four mentioned algorithms in comparison to the widely known differential evolution (DE), genetic algorithms (GAs), harmony search (HS), and particle swarm optimization (PSO) algorithms when applied to the problem of unconstrained nonlinear continuous function optimization. …”
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Simulated evolution for timing and low power VLSI standard cell placement by Sait, Sadiq M.

“…Abstract This paper presents a Fuzzy Simulated Evolution algorithm for VLSI standard cell placement with the objective of minimizing power, delay and area. …”
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Fuzzy simulated evolution for power and performance optimization ofVLSI placement by Sait, Sadiq M.

“…This is a hard multiobjective combinatorial optimization problem with no known exact and efficient algorithm that can guarantee finding a solution of specific or desirable quality. …”
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Cooperative Co-evolution using LSHADE with Restarts For The CEC15 Benchmarks by El-Abd, Mohammed

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Improved Reptile Search Algorithm by Salp Swarm Algorithm for Medical Image Segmentation by Abualigah, Laith

“…This study proposes a novel nature-inspired meta-heuristic optimizer based on the Reptile Search Algorithm combed with Salp Swarm Algorithm for image segmentation using gray-scale multi-level thresholding, called RSA-SSA. …”
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Minimizing using BBO and DFO methods by El Zeghondy, Jean

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Fast force-directed/simulated evolution hybrid for multiobjective VLSI cell placement by Sait, Sadiq M.

“…In this work, a fast hybrid algorithm is designed to address this problem. The algorithm employs simulated evolution (SE), an iterative search heuristic that comprises three steps: evaluation, selection and allocation. …”
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A simulated evolution approach to task-matching and scheduling in heterogeneous computing environments by Barada, Hassan

“…The performance of SE is compared with a genetic algorithm approach for the same problem with respect to the quality of solutions generated, and timing requirements of the algorithms. r 2003 Elsevier Science Ltd. …”
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Incremental Genetic Algorithm by Mansour, Nashat

“…Classical Genetic Algorithms (CGA) are known to find good sub-optimal solutions for complex and intractable optimization problems. …”
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Topics in graph algorithms by Abu-Khzam, Faisal Nabih

“…Coping with computational intractability has inspired the development of a variety of algorithmic techniques. The main challenge has usually been the design of polynomial time algorithms for NP-complete problems in a way that guarantees some, often worst-case, satisfactory performance when compared to exact (optimal) solutions. …”
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