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In a deregulated power industry, accurate short term load forecasting (STLF) and price forecasting (STPF) is a key issue in daily power market. The load forecasting helps in unit commitment as well as in economic scheduling of the generators. The price forecasting helps an electric utility to make important decisions like generation of electric power, bidding for generation, price switching and infrastructure...
Artificial plant optimization algorithm is proposed to solve constrained optimization problems in this paper. In APOA, a shrinkage coefficient is introduce to ensure that all dimensions of a branch are within lower and upper bounds, and a new function to determine whether the particle is within the feasible region. One dimensional search optimization methods are selected in algorithm to produce a...
Structural optimization of Lennard-Jones clusters (LJ) plays an important role in theoretical analysis of physics and chemistry due to the exponential increased local optima. In this paper, a new evolutionary algorithm which is inspired by the plant growing process is introduced to solve this problem. It employs the photosynthesis operator and phototropism operator to mimic photosynthesis and phototropism...
In this paper, a new stochastic optimization algorithm is introduced to simulate the plant growing process. It employs the photosynthesis operator and phototropism operator to mimic photosynthesis and phototropism phenomenon. For the plant growing process, photosynthesis is a basic mechanism to provide the energy from sunshine, while phototropism is an important character to guide the growing direction...
In particle swarm optimization algorithm, computational efficiency is one key problem to affect the algorithm performance due to the dynamic balance between exploration and exploitation capabilities. In this paper, a new strategy, one group-decided position is regarded as the attraction center to provide more chances to search the local optimum. This group-decided position is estimated with all particles'...
Social emotional optimization algorithm (SEOA) is a novel swarm intelligent population-based optimization algorithm by simulating the human social behaviors. How- ever, as a stochastic optimization algorithm, it is easily trapped into local optima. In this paper, we propose a new hybrid algorithm combining with Metropolis rule to enhance the escaping capability from local optima. Further- more, the...
Social emotional optimization algorithm (SEOA) is a novel swarm intelligent population-based optimization algorithm by simulating the human social behaviors. However, it's diversity is decreased increased when solving high-dimensional multi-modal optimization problems. Therefore, in this paper, a new hybrid SEOA with Metropolis rule is introduced to enhance the exploration capability. To test the...
As a nonlinear time-varying system, it is hard to make the stability analysis for particle swarm optimization (PSO). In this paper, geometric speed stability analysis is introduced to analyze the stability condition of PSO. According to the condition, a new inertia weight selection strategy is proposed. Simulation results show it is effective.
Hungry particle swarm optimization (HPSO) is a new novel variant of particle swarm optimization that incorporating the living pressure for each particle. In HPSO, total particles have three different movement motions according to different living pressures. In this paper, HPSO is employed to solve the directing orbits of chaotic systems, simulation results show this new variant increases the performance...
A reactive power optimization is a multi-modal, mixed-variable, multi-constraint and nonlinear planning problem. In the last decades, many computational intelligence-based techniques have been proposed for reactive power optimization problem, such as genetic algorithm (GA), particle swarm optimization (PSO), differential evolution (DE), Tabu search. Recently, a new swarm intelligent algorithm, social...
Structural optimization of Lennard-Jones (LJ) clusters is a classical NP problem. There are many local minima locating near the global minimum, and the local optima number is increased exponentially. Social cognitive optimization algorithm (SCOA) is a new swarm intelligent technique by simulating the human society. However, its local search capability is still weak. Therefore, in this paper, a novel...
Stochastic dynamic step length particle swarm optimization (SDSLPSO) is a new novel variant of particle swarm optimization that incorporating the dynamic step length for each particle in each iteration. This strategy simulates the phenomenon that each bird adjusts its velocity automatically in the process to finding the prey. In this paper, SD-SLPSO is employed to solve the directing orbits of chaotic...
Finding feasible solution for a nonlinear equations is a very challenging problem and generally needs a high computational efforts. In this paper, a new swarm intelligent algorithm, social cognitive optimization algorithm (SCOA), is proposed to solve this problem. In SCOA, each individual simulates one natural person. All of them are communicated through cooperation and competition to increase social...
Group search optimizer (GSO) is a new novel swarm intelligent technique by simulating animal behavioral ecology. However, as a stochastic optimization algorithm, it is still easily trapped into local optima when dealing with multi-modal optimization problems. Therefore, in this paper, a new variant of GSO is designed by hybriding Metropolis rule to further enhancing the capability escaping from local...
A series of phenylazo-β-naphthol-containing sulfonamide disperse dyes were prepared from C.I. Acid Orange 7 by successive reactions of chlorination and amination, and their chemical structures were characterized by FTIR, 1H NMR, and mass spectrometry. The dyes were applied to coloring of knitted fabrics from fine denier polypropylene fibers by exhaust dyeing and their optimal dyeing conditions, such...
Stochastic particle swarm optimization is a novel variant of particle swarm optimization that convergent to the global optimum with probability one. However, the local search capability is not always well in some cases, therefore, in this paper, a technique, dynamic step length, is incorporated into the structure of stochastic particle swarm optimization aiming to further improve the performance....
This paper presents a general framework of physics-inspired method named artificial physics optimization (APO) Algorithm, a population-based, stochastic for multidimensional search and optimization. APO invokes a gravitational metaphor in which the force of gravity may be attractive or repulsive, the aggregate effect of which is to move particles toward local and global optima. APO's particles (solutions...
Particle swarm optimization (PSO) is a new robust swarm intelligence technique, which has exhibited good performance on well-known numerical test problems. Though many improvements published aims to increase the computational efficiency, there are still many works need to do. Inspired by evolutionary programming theory, this paper proposes a self-adaptive particle swarm optimization in which the velocity...
Cognitive learning factor is an important parameter in particle swarm optimization algorithm(PSO). Although many selection strategies have been proposed, there is still much work need to do. Inspired by the black stork foraging process, this paper designs a new cognitive selection strategy, in which the whole swarm is divided into adult and infant particle, and each kind particle has its special choice...
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