In this paper, a new nature-inspired population-based meta-heuristic algorithm is presented. The algorithm, called Cyclical Parthenogenesis algorithm (CPA), is inspired by reproduction and social behavior of some zoological species like aphids, which can reproduce with and without mating. The algorithm considers each candidate solution as a living organism and iteratively improves the quality of solutions utilizing reproduction and displacement mechanisms. Mathematical and engineering design problems are employed in order to investigate the viability of the proposed algorithm. The results indicate that the performance of the newly proposed algorithm is comparable to other state-of-the-art meta-heuristic algorithms.

Purpose: In recent years, meta-heuristic algorithms and their application in solving complicated, nonlinear, and high dimensions problems have increased dramatically and the fact that meta-heuristic algorithms are used to solve complex and changing problems of real life, has caused the algorithms world and their design to be very dynamic and alive, that's why new algorithms are constantly being created. Hence, the purpose of this research is to introduce a novel meta-heuristic algorithm called Military Optimization algorithm (MOA). Methodology: Inspired by military operations, the proposed algorithm was designed and presented. After coding, Standard test functions and benchmark algorithms were determined to evaluate the performance of the algorithm. Findings: The performance of new algorithm is analyzed by 23 standard test functions and compared to 8 benchmark meta-heuristic algorithms including: Genetic algorithm, Particle Swarm Optimization, Artificial Bee Colony, Shuffled Frog Leaping algorithm, and Imperialist Competitive algorithm, Grey Wolf Optimizer, Whale Optimization algorithm, and Grasshopper Optimization algorithm, by considering three indices of "average answers", "time complexity of algorithm (speed)" and "Convergence speed/ time". The results show the excellent performance of the proposed algorithm. Originality/Value: In this paper, inspired by military operations, a novel meta-heuristic algorithm called MOA is introduced. It is population-based and stable with "random search", "dividing solution space into several regions and allocating a part of the population to each region", "cavalry search", and "infantry search".

RNAs play a fundamental role in many biological and medical processes and the activity of RNA is directly dependent to itsstructure. Designing RNA structures is a basic problem in biology that is important in the treatment and nanotechnology. In this regard، some algorithms have been formed to predict RNA secondary structure. In this paper، we present an algorithm to accurately predict RNA secondary structure based on minimum free energy and maximum number of adjacent base pairs. This algorithm stands on a heuristic approach، which employs a dot matrix representation of all possible base pairs in RNA. Afterward، stems are extracted from the dot matrix and decreasingly sorted based on their length. Then the stems with equal length are increasingly sorted according to the free energy. Finally، the stems are orderly selected to form RNA secondary structure. The proposed algorithm is performed on some datasets containing CopA، CopT، R1inv، R2inv، Tar، Tar*، DIS، IncRNA54، and RepZ in the bacteria. Experimental results showed high accuracy of 95. 71% of the proposed algorithm. This algorithm is run in lower computational time in comparison to the other similar approaches.

Vehicle routing problem is very important and logistic in combinatorial optimization. In this paper, an innovative algorithm that combines the colony of ants and mutation operation for vehicle routing problem is presented.

Optimization problems are becoming more complicated, and their resource requirements are rising. Real-life optimization problems are often NP-hard and time or memory consuming. Nature has always been an excellent pattern for humans to pull out the best mechanisms and the best engineering to solve their problems. The concept of optimization seen in several natural processes, such as species evolution, swarm intelligence, social group behavior, the immune system, mating strategies, reproduction and foraging, and animals’ cooperative hunting behavior. This paper proposes a new Meta-heuristic algorithm for solving NP-hard nonlinear optimization problems inspired by the intelligence, socially, and collaborative behavior of the Qashqai nomad’s migration who have adjusted for many years. In the design of this algorithm uses population-based features, experts’ opinions, and more to improve its performance in achieving the optimal global solution. The performance of this algorithm tested using the well-known optimization test functions and factory facility layout problems. It found that in many cases, the performance of the proposed algorithm was better than other known meta-heuristic algorithms in terms of convergence speed and quality of solutions. The name of this algorithm chooses in honor of the Qashqai nomads, the famous tribes of southwest Iran, the Qashqai algorithm

This paper presents a model for minimizing the makespan in the flow shop scheduling problem. Due to the impact of increased workloads, flow shops are becoming more popular and widely used in industries. To solve the challenge of minimizing makespan, a Hybrid-heuristic-Metaheuristic-Genetic-algorithm (HHMGA) is proposed. The proposed HHMGA algorithm is tested using the simulation software and demonstrated with steel industry data. The results are compared with those of the best available flow shop problem algorithms such as Palmer’s slope index, Campbell-Dudek-Smith (CDS), Nawaz-Enscore-Ham (NEH), genetic algorithm (GA) and particle swarm optimization (PSO). According to empirical results and relative differences from the lower bound, the proposed technique outperforms the three heuristics and two metaheuristics algorithms in three of six cases, while the remaining three produce the same results as the NEH heuristic. In comparison to the steel industry's regular job scheduling technique, the simulation model based on HHMGA can save 4642 hours. It was discovered that the suggested model enhanced the job sequence based on the makespan requirements.

Task assignment problem (TAP) involves assigning a number of tasks to a number of processors in distributed computing systems and its objective is to minimize the sum of the total execution and communication costs, subject to all of the resource constraints. TAP is a combinatorial optimization problem and NP-complete. This paper proposes a hybrid meta-heuristic algorithm for solving TAP in a heterogeneous distributed computing system. To compare our algorithm with previous ones, an extensive computational study on some benchmark problems was conducted. The results obtained from the computational study indicate that the proposed algorithm is a viable and effective approach for the TAP.