Increasing population and food demand, disproportionate cultivation and annual production of various agricultural products with market needs and low productivity of the agricultural sector and the loss of water and soil resources have made it necessary to determine and implement the country's optimal cropping pattern. In this study, due to the limitations and problems of classical methods in order to reduce processing time and improve the quality of solutions, the Multi-Objective Chaotic Particle swarm optimization was used to determine the optimal cultivation pattern of Sistan plain in optimal conditions and deficit irrigation. The results of the Multi-Objective Chaotic Particle swarm optimization for the dominant cultures in the region showed that the current cropping pattern of the region is not optimal and with the implementation of the proposed model, the profit per unit area under cultivation will increase. The results of application of deficit irrigation during different growing periods of wheat, barley, alfalfa, sorghum, watermelon and grapes showed that applying deficit irrigation in this plain is not a good strategy and therefore only a full irrigation strategy is recommended. The results of sensitivity analysis of the model showed that at low prices, farmers reaction is less and at higher prices more reaction to price changes and with increasing prices, the program efficiency is lower.

Particle swarm optimization (PSO) is a metaheuristic optimization algorithm that owes much of its allure to its simplicity and its high effectiveness in solving sophisticated optimization problems. However, since the performance of the standard PSO is prone to being trapped in local extrema, abundant variants of PSO have been proposed by far. For instance, Fuzzy Adaptive PSO (FAPSO) algorithms have been being studied extensively in recent years. In this study, a modified version of PSO algorithms is presented and is named as Adaptive Particularly Tunable Fuzzy Particle swarm optimization (APT-FPSO). In it, the global and personal learning coefficients of every single Particle are tuned adaptively and particularly, at an individual extent, within each iteration with the aid of fuzzy logic concepts. Ample statistical evidence is provided indicating that the proposed algorithm further improves the potentialities and capabilities of the standard PSO.

In this paper, we propose a novel algorithm to enhance the noisy speech in the framework of dual-channel speech enhancement. The new method is a hybrid optimization algorithm, which employs the combination of the conventional q-PSO and the shuffled subswarms Particle optimization (SSPSO) technique. It is known that the q-PSO algorithm has better optimization performance than standard PSO algorithm, when dealing with some simple benchmark functions. To improve further the performance of the conventional PSO, the SSPSO algorithm has been suggested to increase the diversity of Particles in the swarm. The proposed speech enhancement method, called q-SSPSO, is a hybrid technique, which incorporates both q-PSO and SSPSO, with the goal of exploiting the advantages of both algorithms. It is shown that the new q-SSPSO algorithm is quite effective in achieving global convergence for adaptive filters, which results in a better suppression of noise from input speech signal. Experimental results indicate that the new algorithm outperforms the standard PSO, q-PSO, and SSPSO in a sense of convergence rate and SNR-improvement.

optimization of the volume/weight in the gear train is of great importance for industries and researchers. In this paper, using the Particle swarm optimization algorithm, a general gear train is optimized. The main idea is to optimize the volume/weight of the gearbox in 3 directions. To this end, the optimization process based on the PSO algorithm occurs along the height, length, and width of the gearbox to achieve the smallest possible gearbox. The constraints are divided into three types named geometrical, design and control constraints. The optimization process is presented for two and three-stage gear trains and by choosing different values for the gear ratio, input power and hardness of gears. The practical graphs for the optimum value of the weight/volume and all necessary design parameters of gearbox such as the number of stages, position, modulus of gears, face width of gears, and diameter of shafts are also presented. The results are validated by comparing with the results reported in the previous publications.

Optimal operation of multipurpose reservoirs is one of the complex and sometimes nonlinear problems in the field of multi-objective optimization. Evolutionary algorithms are optimization tools that search decision space using simulation of natural biological evolution and present a set of points as the optimum solutions of problem.In this research, application of multi-objective Particle swarm optimization (MOPSO) in optimal operation of Bazoft reservoir with different objectives, including generating hydropower energy, supplying downstream demands (drinking, industry and agriculture), recreation and flood control have been considered. In this regard, solution sets of the MOPSO algorithm in bi-combination of objectives and compromise programming (CP) using different weighting and power coefficients have been first compared that the MOPSO algorithm in all combinations of objectives is more capable than the CP to find solution with appropriate distribution and these solutions have dominated the CP solutions. Then, ending points of solution set from the MOPSO algorithm and nonlinear programming (NLP) results have been compared. Results showed that the MOPSO algorithm with 0.3 percent difference from the NLP results has more capability to present optimum solutions in the ending points of solution set.

One of the port planning problems that has been noticed in many papers and research is the berth planning problems. Berth planning includes two sub-problems; Berth Allocation Problem (BAP) and Quay Crane Assignment Problem (QCAP). This paper develops one mathematical model by integrating these two sub-problems. The berth allocation and quay crane assignment model (BAQCAP) is solved by two metaheuristic algorithms; Taboo Search (TS) and Ant Colony optimization (ACO). On the other hand, the berth plan is located in a disturbed environment; unexpected events may occur during the execution of the plan, making it infeasible or challenging to do the initial berth plan. These unexpected events are known as disruptions, which can impose additional costs on the port or make the initial berth plan infeasible. For this reason, The primary purpose of this paper is on the berth plan recovery in the disrupted situation. the Berth plan is recovered with two methods; Global recovery and local recovery. This paper compares global and local recovery to identify the optimal method for berth plan recovery. The numerical results show the optimal performance in the local recovery method. In this paper, the data from Shahid Rajaei port is used.