Multi-objective Cultural Algorithm Research Articles

An Optimal Routing Protocol Using Multi-Objective Cultural Algorithm for Wireless Sensor Networks (ORPMCA)

Wireless Sensor Networks (WSNs) is one of the most important types of networks in the world where sensors collect information within a specific area and send the data to a central point called the Base Station (BS). Many researchers have attempted to develop or improve the performance of WSNs by trying to mitigate the limitations and challenges facing WSNs. The most important challenge is to reduce energy consumption in a limited battery power supply of sensor nodes. Energy is consumed by sensor nodes in WSNs to perform three significant functions namely data sensing, transmitting and relaying. Various energy-saving routing protocols have been proposed to solve this problem to maintain the lifetime of the network for longer periods. In this paper, we propose an optimal routing protocol using multi-objective cultural algorithm for wireless sensor networks (ORPMCA). The simulation results showed that the ORPMCA protocol extends the lifetime of the WSN compared to other protocols by 15%.

An improved multiobjective cultural algorithm with a multistrategy knowledge base

An improved multiobjective cultural algorithm with a multistrategy knowledge base

A unified framework for effective team formation in social networks

Collaboration networks are social networks in which nodes represent experts, and edges represent the interactions between them. Team Formation Problem (TFP) in Social Networks (SN) is to construct a group of individuals to work on complex tasks. Teams should satisfy the skill set required by the tasks and can collaborate effectively under multiple constraints. Although many algorithms have been proposed to confront the TFP, most of them optimize different criteria and various parameters (e.g. communication cost or expertise level). There is no unified framework to incorporate the most significant parameters towards formulating effective teams of experts. We propose a unified framework for the TFP in SN based on a multi-objective cultural algorithm that involves the integration of essential cost functions such as communication cost, expertise level, collective trust score, and geological proximity. Since these are conflicting objectives, we return a set of Pareto front of teams that are not dominated by other feasible teams with regards to any of the objectives. Moreover, we examine the temporal nature of both communication costs and expertise levels in our model and introduce a new method to formulate them. We introduce a profile similarity formula to express the trust score. We then discuss the importance of emotional index in TFP. Our model is tested on a benchmark table, which is generated with various criteria of social networks. Our model is then compared with NSGA II, Graph-Based and Exhaustive search.

Uncertainty management in multiobjective hydro-thermal self-scheduling under emission considerations

In this paper, a stochastic multiobjective framework is proposed for a day-ahead short-term Hydro Thermal Self-Scheduling (HTSS) problem for joint energy and reserve markets. An efficient linear formulations are introduced in this paper to deal with the nonlinearity of original problem due to the dynamic ramp rate limits, prohibited operating zones, operating services of thermal plants, multi-head power discharge characteristics of hydro generating units and spillage of reservoirs. Besides, system uncertainties including the generating units’ contingencies and price uncertainty are explicitly considered in the stochastic market clearing scheme. For the stochastic modeling of probable multiobjective optimization scenarios, a lattice Monte Carlo simulation has been adopted to have a better coverage of the system uncertainty spectrum. Consequently, the resulting multiobjective optimization scenarios should concurrently optimize competing objective functions including GENeration COmpany's (GENCO's) profit maximization and thermal units’ emission minimization. Accordingly, the ɛ-constraint method is used to solve the multiobjective optimization problem and generate the Pareto set. Then, a fuzzy satisfying method is employed to choose the most preferred solution among all Pareto optimal solutions. The performance of the presented method is verified in different case studies. The results obtained from ɛ-constraint method is compared with those reported by weighted sum method, evolutionary programming-based interactive Fuzzy satisfying method, differential evolution, quantum-behaved particle swarm optimization and hybrid multi-objective cultural algorithm, verifying the superiority of the proposed approach.

Evaluating the effectiveness of normal boundary intersection method for short-term environmental/economic hydrothermal self-scheduling

The problem of the optimal scheduling of available hydro and thermal generating units considering a short scheduling period (one day-one week) in order to maximize the total profit is denoted as short-term hydro thermal self-scheduling (SHTSS). Mixed-integer linear programming (MILP) method is proposed to model the SHTSS problem in the day-ahead energy and reserve markets. MILP formulation allows for considering a precise model for the prohibited working zones, dynamic ramp rate constraints and operating services of thermal generating units, as well as the characteristics of multi-head power discharge for hydro generating units and reservoirs’ spillage. This problem is modelled as a multi-objective (MO) optimization one, having two objectives, i.e. maximization of the profit of Generation Company's (GENCO's) and minimization of emissions from thermal units. In order to solve the problem and generate the non-inferior solutions, normal boundary intersection (NBI) method is applied. The main advantage is the provision of set of uniformly distributed non-dominated solutions regardless of the scales of objective functions values. Then, a fuzzy based decision maker is employed in order to select a non-inferior solution. In order to demonstrate the effectiveness of the presented method, several numerical simulations are presented. Furthermore, the obtained results are compared with those obtained considering different methods for obtaining non-inferior solutions, such as weighted sum method, evolutionary programming-based interactive fuzzy satisfying method, differential evolution, particle swarm optimization and hybrid multi-objective cultural algorithm.

Economic environmental dispatch using an enhanced multi-objective cultural algorithm

Economic environment dispatch (EED) is a significant optimization problem in conventional fossil fuel fired power system to distribute load demand reasonably and scientifically so that fuel cost and emission issues are optimized simultaneously while satisfying various constraints. Valve-point effect, prohibited operation zones (POZs) and transmission losses make the EED a non-smooth and non-convex optimization problem. In order to solve this constrained multi-objective optimization problem including competing objectives as well as complex constraints, an enhanced multi-objective cultural algorithm (EMOCA) is proposed in this paper. The proposed method combines cultural algorithm framework with particle swarm optimization (PSO) to carry though the evolution of population space. Besides, two knowledge structures of belief space in CA are redefined according to the features of EED problem. Moreover, an effective constrain handling method is proposed to handle the equality and inequality constraints of EED problem. Simulation results of test systems demonstrate the capability of the proposed algorithm to generate well-distributed Pareto optimal solutions in a single run. Compared with some current methods, EMOCA has a good performance in finding a diverse set of solutions and in converging near the true Pareto optimal front with lower fuel cost and emission issues synthetically.

Nugget Discovery with a Multi-Objective Cultural Algorithm

Partial classification popularly known as nugget discovery comes under descriptive knowledge discovery. It involves mining rules for a target class of interest. Classification "If-Then" rules are the most sought out by decision makers since they are the most comprehensible form of knowledge mined by data mining techniques. The rules have certain properties namely the rule metrics which are used to evaluate them. Mining rules with user specified properties can be considered as a multi-objective optimization problem since the rules have to satisfy more than one property to be used by the user. Cultural algorithm (CA) with its knowledge sources have been used in solving many optimization problems. However research gap exists in using cultural algorithm for multi-objective optimization of rules. In the current study a multi-objective cultural algorithm is proposed for partial classification. Results of experiments on benchmark data sets reveal good performance.

Multi-objective optimization of hydrothermal energy system considering economic and environmental aspects

Along with continuous global warming, the environmental problems, besides the economic objective, are expected to play more and more important role in the operation of hydrothermal power system. In this paper, the short-term multi-objective economic environmental hydrothermal scheduling (MEEHS) model is developed to analyze the operating approach of MEEHS problem, which simultaneously optimize energy cost as well as the pollutant emission effects. Meanwhile, transmission line losses among generation units, valve-point loading effects of thermal units and water transport delay between hydraulic connected reservoirs are taken into consideration in the problem formulation. In order to solve MEEHS problem, a new multi-objective cultural algorithm based on particle swarm optimization (MOCA-PSO) is presented in way of combining the cultural algorithm framework with particle swarm optimization (PSO) to carry though the evolution of population space. Furthermore, an effective constrain handling method is proposed to handle the operational constraints of MEEHS problem. The proposed method is applied to a hydrothermal power system consisting of four hydro plants and three thermal units for the case studies. Compared with several previous methods, the simulation solutions of MOCA-PSO with smaller fuel cost and lower emission effects proves that it can be an alternative method to deal with MEEHS problems. The obtained results demonstrate that the change of optimization objective leads to the shift of optimal operation schedules. Finally, the scheduling results of MEEHS problem offer enough choices to the decision makers. Thus, the operation with better performance of environment is achieved by more energy system cost.

A hybrid multi-objective cultural algorithm for short-term environmental/economic hydrothermal scheduling

The short-term environmental/economic hydrothermal scheduling (SEEHS) with the consideration of multiple objectives is a complicated non-linear constrained optimization problem with non-smooth and non-convex characteristics. In this paper, a multi-objective optimization model of SEEHS is proposed to consider the minimal of fuel cost and emission effects synthetically, and the transmission loss, the water transport delays between connected reservoirs as well as the valve-point effects of thermal plants are taken into consideration to formulate the problem precisely. Meanwhile, a hybrid multi-objective cultural algorithm (HMOCA) is presented to deal with SEEHS problem by optimizing both two objectives simultaneously. The proposed method integrated differential evolution (DE) algorithm into the framework of cultural algorithm model to implement the evolution of population space, and two knowledge structures in belief space are redefined according to the characteristics of DE and SEEHS problem to avoid premature convergence effectively. Moreover, in order to deal with the complicated constraints effectively, new heuristic constraint handling methods without any penalty factor settings are proposed in this paper. The feasibility and effectiveness of the proposed HMOCA method are demonstrated by two case studies of a hydrothermal power system. The simulation results reveal that, compared with other methods established recently, HMOCA can get better quality solutions by reducing fuel cost and emission effects simultaneously.

Multi-Population Multi-Objective Cultural Algorithm

In existing multi-population multi-objective cultural algorithms, information are exchanged among sub-populations by individuals. However, migrated individuals can not reflect the evolution information enough, which limits the evolution performance.In order to enhance the migration efficiency, a novel multi-population multi-objective cultural algorithm adopting knowledge migration is proposed. Implicit knowledge extracted from the evolution process of each sub-population directly reflects the information about dominant search space. By migrating the knowledge among sub-populations at the constant interval, the algorithm realizes more effective interaction with less communication cost. Taken benchmark functions as the examples, simulation results indicate that the algorithm can effectively obtain the Pareto-optimal sets of multi-objective optimization problems. The distribution performance is also improved.