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Optimization is a process that is followed to improve an objective function. The aim of the optimization is to improve the solution of a problem developed by a scientist or an engineer. In any optimization algorithm, the initial conditions have a big effect on quality of the final found solution. One of the new optimization algorithms is See-See Partridge Chicks Optimization (SSPCO) algorithm, which is modeled upon the behavior of the partridge chicks. Chaotic behaviors are frequent in the natural phenomena. Indeed, many real world behaviors which resemble random, are chaotic. As these behaviors are highly sensitive to the initial conditions, they show such really random-like behaviors that can't be predicted (while they are not random at all). In this paper, we have implemented a chaotic SSPCO (CSSPSO) algorithm. We have used two different chaotic functions and we have lastly compared the results with the state of the art algorithms. The results indicate that chaos has a positive effect and the CSSPCO algorithm outperforms the state of the art algorithms. [ABSTRACT FROM AUTHOR]

Nature is a huge source of inspiration for solving difficult and complex problems in science. Nature-based problem solvers always find an optimal solution (whether local or global) to any given problem; so they are sometimes considered as black-box problem solvers. Meta-heuristic algorithms that are inspired by nature (through imitating the nature) have opened a new approach to solving optimization problems. In the past decades, numerous research efforts have been focused in this specific area. In this paper, an optimization algorithm inspired by the nature has been introduced which is modeled from the behavior of the chicks of a type of bird called see-see partridge. We name the algorithm SSPCO. SSPCO optimization algorithm is an algorithm similar to the algorithm of particle swarm optimization (PSO) that the motion equation and the variables' velocity are modeled from the behavior of this type of bird. Simulation of the algorithm was done with MATLAB software and the results of the 6 primary known algorithms and 8 advanced ones were compared with 14 unconstrained continuous functions. The results indicate that the proposed algorithm is an efficient algorithm comparing the others. [ABSTRACT FROM AUTHOR]

• Optimal management (technical and economic) of generation expansion planning. • Improve the performance of generation expansion planning based on demand response programs. • Reduce operating costs of generation expansion planning based on models of probability distribution functions. • Increase the reliability and security of distribution networks based on the adaptive particle swarm optimization algorithm. Generation expansion planning in the power system is of particular importance. In traditional systems, investment in the generation expansion was made by the electricity company, but with the restructuring in the electricity industry, the owners of different parts of the system submit their proposals to the independent system operator and the independent system operator chooses the optimal design. Slowly increasing energy production from renewable sources can pose challenges for the grid. Increasing the penetration of renewable resources due to uncertainty in their production can reduce network reliability and thus increase system costs. The investigation on generation expansion planning is a multifaceted issue (technical and economic) that has been analyzed in various aspects in recent years. In this study, a multidimensional structure of generation expansion planning based on increasing the penetration level of distributed generation resources (renewable and non-renewable) as well as the application of load management and demand response algorithms is proposed. The proposed model is scheduled based on two levels of primary and secondary development. In the primary, the development of generation and transmission based on large-scale power plants as well as solar and wind farms are presented. In the secondary, in order to reduce the power fluctuations caused by the distributed generation's units, non-stochastic power generation units such as micro turbines, gas turbines and combined heat and power have been utilized. To overcome the difficulties in solving the problem of hybrid and non-convergent mixed-integer problem, the adaptive particle swarm optimization has been hired. The simulation results indicate that in the second scenario, where the development of the generation expansion planning is based on the integration of distributed generation resources and power plants, it is more cost-effective. In addition to, these simulation results represent the accuracy of the proposed probabilistic method in planning of dynamic generation systems in order to estimate the probability density function and the optimal output variables in multi-objective techno-economic planning. [ABSTRACT FROM AUTHOR]