Mohammed Hadwan^1,2,3,*

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5545-5559, 2022, DOI:10.32604/cmc.2022.024512

Abstract A real-life problem is the rostering of nurses at hospitals. It is a famous nondeterministic, polynomial time (NP) -hard combinatorial optimization problem. Handling the real-world nurse rostering problem (NRP) constraints in distributing workload equally between available nurses is still a difficult task to achieve. The international shortage of nurses, in addition to the spread of COVID-19, has made it more difficult to provide convenient rosters for nurses. Based on the literature, heuristic-based methods are the most commonly used methods to solve the NRP due to its computational complexity, especially for large rosters. Heuristic-based algorithms in general have problems striking the… More >

Betania Hernández-Ocaña, Adrian García-López, José Hernández-Torruco, Oscar Chávez-Bosquez^*

Intelligent Automation & Soft Computing, Vol.32, No.3, pp. 1797-1813, 2022, DOI:10.32604/iasc.2022.023570

Abstract The Bacterial Foraging Algorithm (BFOA) is a well-known swarm collective intelligence algorithm used to solve a variety of constraint optimization problems with wide success. Despite its universality, implementing the BFOA may be complex due to the calibration of multiple parameters. Moreover, the Two-Swim Modified Bacterial Foraging Optimization Algorithm (TS-MBFOA) is a state-of-the-art modification of the BFOA which may lead to solutions close to the optimal but with more parameters than the original BFOA. That is why in this paper we present the design using the Unified Modeling Language (UML) and the implementation in the MATLAB platform of a front-end for… More >

Fatemeh Ahmadi Zeidabadi¹, Sajjad Amiri Doumari¹, Mohammad Dehghani², Zeinab Montazeri³, Pavel Trojovský^4,*, Gaurav Dhiman⁵

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5631-5649, 2022, DOI:10.32604/cmc.2022.021072

Abstract Optimization plays an effective role in various disciplines of science and engineering. Optimization problems should either be optimized using the appropriate method (i.e., minimization or maximization). Optimization algorithms are one of the efficient and effective methods in providing quasi-optimal solutions for these type of problems. In this study, a new algorithm called the Mutated Leader Algorithm (MLA) is presented. The main idea in the proposed MLA is to update the members of the algorithm population in the search space based on the guidance of a mutated leader. In addition to information about the best member of the population, the mutated… More >

R. Manjula Devi¹, M. Premkumar², Pradeep Jangir³, Mohamed Abdelghany Elkotb^4,5, Rajvikram Madurai Elavarasan⁶, Kottakkaran Sooppy Nisar^7,*

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4803-4827, 2022, DOI:10.32604/cmc.2022.020847

Abstract Optimization is a key technique for maximizing or minimizing functions and achieving optimal cost, gains, energy, mass, and so on. In order to solve optimization problems, metaheuristic algorithms are essential. Most of these techniques are influenced by collective knowledge and natural foraging. There is no such thing as the best or worst algorithm; instead, there are more effective algorithms for certain problems. Therefore, in this paper, a new improved variant of a recently proposed metaphorless Runge-Kutta Optimization (RKO) algorithm, called Improved Runge-Kutta Optimization (IRKO) algorithm, is suggested for solving optimization problems. The IRKO is formulated using the basic RKO and… More >

Fatemeh Ahmadi Zeidabadi¹, Sajjad Amiri Doumari¹, Mohammad Dehghani², Zeinab Montazeri², Pavel Trojovský^3,*, Gaurav Dhiman⁴

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2905-2921, 2022, DOI:10.32604/cmc.2022.019867

Abstract There are many optimization problems in different branches of science that should be solved using an appropriate methodology. Population-based optimization algorithms are one of the most efficient approaches to solve this type of problems. In this paper, a new optimization algorithm called All Members-Based Optimizer (AMBO) is introduced to solve various optimization problems. The main idea in designing the proposed AMBO algorithm is to use more information from the population members of the algorithm instead of just a few specific members (such as best member and worst member) to update the population matrix. Therefore, in AMBO, any member of the… More >

Adnan Ashraf¹, Abdulwahab Ali Almazroi², Waqas Haider Bangyal^3,*, Mohammed A. Alqarni⁴

Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 191-206, 2022, DOI:10.32604/iasc.2022.015810

Abstract Particle Swarm Optimization (PSO) is a well-known extensively utilized algorithm for a distinct type of optimization problem. In meta-heuristic algorithms, population initialization plays a vital role in solving the classical problems of optimization. The population’s initialization in meta-heuristic algorithms urges the convergence rate and diversity, besides this, it is remarkably beneficial for finding the efficient and effective optimal solution. In this study, we proposed an enhanced variation of the PSO algorithm by using a quasi-random sequence (QRS) for population initialization to improve the convergence rate and diversity. Furthermore, this study represents a new approach for population initialization by incorporating the… More >

Fatimah O. Albalawi, Mashael S. Maashi^*

Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 39-52, 2021, DOI:10.32604/iasc.2021.015657

Abstract In the software field, a large number of projects fail, and billions of dollars are spent on these failed projects. Many software projects are also produced with poor quality or they do not exactly meet customers’ expectations. Moreover, these projects may exceed project budget and/or time. The complexity of managing software development projects and the poor selection of software development life cycle (SDLC) models are among the top reasons for such failure. Various SDLC models are available, but no model is considered the best or worst. In this work, we propose a new methodology that solves the SDLC optimization problem… More >

Muhammad Asif Jan^1,*, Yasir Mahmood¹, Hidayat Ullah Khan², Wali Khan Mashwani¹, Muhammad Irfan Uddin³, Marwan Mahmoud⁴, Rashida Adeeb Khanum⁵, Ikramullah⁶, Noor Mast³

CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 2845-2862, 2021, DOI:10.32604/cmc.2021.015294

Abstract The particle swarm optimization (PSO) algorithm is an established nature-inspired population-based meta-heuristic that replicates the synchronizing movements of birds and fish. PSO is essentially an unconstrained algorithm and requires constraint handling techniques (CHTs) to solve constrained optimization problems (COPs). For this purpose, we integrate two CHTs, the superiority of feasibility (SF) and the violation constraint-handling (VCH), with a PSO. These CHTs distinguish feasible solutions from infeasible ones. Moreover, in SF, the selection of infeasible solutions is based on their degree of constraint violations, whereas in VCH, the number of constraint violations by an infeasible solution is of more importance. Therefore,… More >

Yue Ruan^{1, *}, Samuel Marsh², Xilin Xue¹, Zhihao Liu³, Jingbo Wang^{2, *}

CMC-Computers, Materials & Continua, Vol.63, No.3, pp. 1237-1247, 2020, DOI:10.32604/cmc.2020.010001

Abstract The Quantum Approximate Optimization Algorithm (QAOA) is an algorithmic framework for finding approximate solutions to combinatorial optimization problems. It consists of interleaved unitary transformations induced by two operators labelled the mixing and problem Hamiltonians. To fit this framework, one needs to transform the original problem into a suitable form and embed it into these two Hamiltonians. In this paper, for the well-known NP-hard Traveling Salesman Problem (TSP), we encode its constraints into the mixing Hamiltonian rather than the conventional approach of adding penalty terms to the problem Hamiltonian. Moreover, we map edges (routes) connecting each pair of cities to qubits,… More >

Chung-Lun Kuo, Chein-Shan Liu, Jiang-Ren Chang

The International Conference on Computational & Experimental Engineering and Sciences, Vol.19, No.2, pp. 35-36, 2011, DOI:10.3970/icces.2011.019.035

Abstract In this study, the exponentially convergent scalar homotopy algorithm (ECSHA) is proposed to solve the nonlinear optimization problems under equality and inequality constraints. The Kuhn-Tucker optimality conditions associated with NCP-functions are adopted to transform the nonlinear optimization problems into a set of nonlinear algebraic equations. Then the ECSHA is used to solve the resultant nonlinear equations. The proposed scheme keeps the merit of the conventional homotopy method, such as global convergence, but the inverse of the Jacobian matrix is avoid with the aid of the scalar homotopy function. Several numerical examples are provided to demonstrate the efficiency of the proposed… More >