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In this oligopolistic electricity market, the maximization of profit for generators is mainly dealt with the used bidding strategies. For selling of electricity with maximum profit power companies required suitable bidding models that includes power operating constraints and price uncertainty within the market. In this paper, we present particle swarm optimization (PSO) algorithms to determine bid prices and quantities under the rules of a competitive power market with using emission as a constraint. The Objective of this paper is the potential impacts of emissions trading on power industries and electricity markets. Increasing environmental issues and regulations have forced Generation companies (GENCOs) to review the policies being used for long term planning. Constraints on CO2 emission are restricted the GENCOs to adopt the green technologies. [ABSTRACT FROM AUTHOR]

In this oligopolistic electricity market, the maximization of profit for generators is mainly dealt with the used bidding strategies. For selling of electricity with maximum profit power companies required suitable bidding models that includes power operating constraints and price uncertainty within the market. In this paper, we present particle swarm optimization (PSO) algorithms to determine bid prices and quantities under the rules of a competitive power market with using emission as a constraint. The Objective of this paper is the potential impacts of emissions trading on power industries and electricity markets. Increasing environmental issues and regulations have forced Generation companies (GENCOs) to review the policies being used for long term planning. Constraints on CO2 emission are restricted the GENCOs to adopt the green technologies. [ABSTRACT FROM AUTHOR]

With the potential growth of multimedia hardware and applications, the machines have to realize the information by adapting to the internal information. An adaptive content based image retrieval (CBIR) approach based on relevance feedback and Firefly algorithm is proposed in this paper. In addition to the color descriptor, wavelet-based texture descriptor is considered to improve the retrieval performance. Feature extraction has been done with the Euclidean distance estimation between the pixels; relevance feedback (RF) based approach but all concerns with the extraction of image accuracy. This research work has a focused approach to increase the performance by optimizing image feature by adopting with the firefly algorithm (FA). The experimental results compared with the other optimization algorithms like particle swarm optimization and genetic algorithm demonstrate the feasibility of the approach. [ABSTRACT FROM AUTHOR]

In this paper, a methodology for combining genetic algorithm (GA) and fuzzy controller (FC) to create a genetic-fuzzy approach (GA-FC) is presented. Using GA, fuzzy rules and membership functions are optimized simultaneously. A water level control process is used to test the performance of GA-FC controller. The process was first modeled using Artificial Neural Network (ANN). FC parameters were then tuned using genetic algorithm. Finally, the performance of GA-FC approach was presented using the simulation and real time applications. The main contribution of the study is that, the developed structure replaces the tedious process of trial and error for better combination of fuzzy parameters and can settle the problem of designing a FC without an expert's experience. [ABSTRACT FROM AUTHOR]

Uncapacitated facility location problem (UFLP) is a combinatorial optimization problem, which has many applications. The artificial fish swarm algorithm has recently emerged in continuous optimization problem. In this paper, we present a simplified binary version of the artificial fish swarm algorithm (S-bAFSA) for solving the UFLP. In S-bAFSA, trial points are created by using crossover and mutation. In order to improve the quality of the solutions, a cyclic reinitialization of the population is carried out. To enhance the accuracy of the solution, a local search is applied on a predefined number of points. The presented algorithm is tested on a set of benchmark uncapacitated facility location problems. [ABSTRACT FROM AUTHOR]

In recent year, the awareness of environmental protection has made the power dispatch model on longer purely economical-oriented. This paper presents a modified particle swarm optimization (MPSO) approach to solve the Unit Commitment (UC) problem for 24 hours at the maximum profit in the power and carbon market. Solving the UC problem for 24 hours in the interconnected power network that is comprised of three independent areas to approach the best dispatching strategy. The UC problem must satisfy the constraints such as the load demand, generating limits, minimum up/down time and ramp rate limits, and also involves determining the limits of power flow, buses voltage and transmission line capacity. The Independent Power Producer (IPP) can obtain the maximum profit by the power and carbon trading amount and calculating power wheeling expense based on the 24-hour power and carbon forecasting trading data. Furthermore, it can also be assessed the basis of participating in the trading market or not. [ABSTRACT FROM AUTHOR]

In recent years, clustering is still a popular analysis tool for data statistics. The data structure identifying from the large-scale data has become a very important issue in the data mining problem. In this paper, an improved particle swarm optimization based on Gauss chaotic map for clustering is proposed. Gauss chaotic map adopts a random sequence with a random starting point as a parameter, and relies on this parameter to update the positions and velocities of the particles. It provides the significant chaos distribution to balance the exploration and exploitation capability for search process. This easy and fast function generates a random seed processes, and further improve the performance of PSO due to their unpredictability. In the experiments, the eight different clustering algorithms were extensively compared on six test data. The results indicate that the performance of our proposed method is significantly better than the performance of other algorithms for data clustering problem. [ABSTRACT FROM AUTHOR]

An improved particle swarm optimizer with inertia weight (PSOIWα) was applied to multi-objective optimization (MOO). In this paper we present a method of multiple particle swarm optimizers with inertia weight (MPSOIWα), which belongs to a kind of the methods of cooperative particle swarm optimization. The crucial idea of the MPSOIWα, here, is to reinforce the search ability of the PSOIWα by the union's power of plural swarms. To demonstrate its effectiveness and search performance, computer experiments on a suite of 2-objective optimization problems are carried out by a weighted sum method. The resulting Pareto-optimal solution distributions corresponding to each given problem indicate that the linear weighted aggregation among the adopted three kinds of dynamically weighted aggregations is the most suitable for ac- quiring better search results. Throughout quantitative analysis to experimental data, we clarify the search characteristics and performance effect of the MPSOIWα contrast with that of the PSOIWα and MPSOIW. [ABSTRACT FROM AUTHOR]

In recent year, the awareness of environmental protection has made the power dispatch model on longer purely economical-oriented. This paper presents a modified particle swarm optimization (MPSO) approach to solve the Unit Commitment (UC) problem for 24 hours at the maximum profit in the power and carbon market. Solving the UC problem for 24 hours in the interconnected power network that is comprised of three independent areas to approach the best dispatching strategy. The UC problem must satisfy the constraints such as the load demand, generating limits, minimum up/down time and ramp rate limits, and also involves determining the limits of power flow, buses voltage and transmission line capacity. The Independent Power Producer (IPP) can obtain the maximum profit by the power and carbon trading amount and calculating power wheeling expense based on the 24-hour power and carbon forecasting trading data. Furthermore, it can also be assessed the basis of participating in the trading market or not. [ABSTRACT FROM AUTHOR]

Particle Swarm Optimization has been an appealing research area for researchers for over 15 years. During these years, a variety of algorithms have been developed around the particle swarm concept. One of these variations is Predator-Prey Particle Swarm Optimization algorithm which is also based on natural swarms which have a hierarchical relationship. Hunting search has also become a new meta-heuristic originating from hunting structure of species. In this paper, a new Particle Swarm Optimization Algorithm has been developed. Animal Food Chain Based Particle Swarm Optimization Algorithm simulates the animal food chain structure in three swarms: omnivores, carnivores and herbivores, in order to balance exploration and exploitation. [ABSTRACT FROM AUTHOR]