This paper applies an evolutionary optimization scheme, inspired by Multi-objective Invasive Weed Optimization (MOIWO) and Non-dominated Sorting (NS) strategies, to find approximate solutions for multiobjective optimal control problems (MOCPs). The desired control function may be subjected to severe changes over a period of time. In response to deficiency, the process of dispersal has been modified in the MOIWO. This modification will increase the explorative power of the weeds and reduces the search space gradually during the iteration process. The performance of the proposed algorithm is compared with conventional Non-dominated Sorting Genetic algorithm (NSGA-II) and Non-dominated Sorting Invasive Weed Optimization (NSIWO) algorithm.

Wireless sensor network a powerful network contains many wireless sensors with limited power resource, data processing, and transmission abilities. Wireless sensor capabilities including computational capacity, radio power, and memory capabilities are much limited. Moreover, to design a hierarchy topology, in addition to energy optimization, find an optimum clusters number and best location of cluster heads are two important issues. Many routing protocols are introduced to discover the optimal routes in order to remove intermediate nodes to reduce the sensors energy consumption. Therefore, for energy consumption optimization in a network, routing protocols and clustering techniques along with composition and aggregation of data are provided. In this paper, to design a hierarchy topology, a Hybrid evolutionary approach, a combination of genetic and imperialist competition algorithms is applied. First, the genetic algorithm is applied to achieve an optimal clusters number where all effective network parameters are taken in into account. Aftermath, the optimal positions of cluster heads inside every cluster are calculated utilizing the imperialist approach. Our results show a significant increment in the network lifetime, lower data-packet lost, higher robust routing compared with standard LEACH and the ICA based LEACH.

In this paper, a new Hybrid decomposition-based multi-objective evolutionary algorithm (MOEA) is proposed for the optimal power flow (OPF) problem including Wind, PV, and PEVs uncertainty with four conflicting objectives. The proposed multi-objective OPF (MOOPF) problem includes minimization of the total cost (TC), total emission (TE), active power loss (APL), and voltage magnitude deviation (VMD) as objectives and a novel constraint handling method, which adaptively adds the penalty function and eliminates the parameter dependence on penalty function evaluation is deployed to handle several constraints in the MOOPF problem. In addition, summation-based sorting and improved diversified selection methods are utilized to enhance the diversity of MOEA. Further, a fuzzy min-max method is utilized to get the best-compromised values from Pareto-optimal solutions. The impact of intermittence of Wind, PV, and PEVs integration is considered for optimal cost analysis. The uncertainty associated with Wind, PV, and PEV systems are represented using probability distribution functions (PDFs) and its uncertainty cost is calculated using the Monte-Carlo simulations (MCSs). A commonly used statistical method called the ANOVA test is used for the comparative examination of several methods. To test the proposed algorithm, standard IEEE 30, 57, and 118-bus test systems were considered with different cases and the acquired results were compared with NSGA-II and MOPSO to validate the suggested algorithm's effectiveness

In this paper, we address the weighted multi-objective re-entrant flow-shop scheduling problem considering release dates in order to minimize makespan, total completion time, total tardiness, maximum idle time, and number of tardy jobs. Each job is taken into account with deterministic processing times, and release dates. The flow-shop comprised of two workshops in whose jobs are entered to the main workshop and after the first part of the processing, they are transferred to the second workshop and after this stage, the jobs are returned to the main workshop for the last part of the processing. We model the problem by a new mixed integer programming based on formulating sum of idle time as a new concept. Moreover, a Hybrid evolutionary algorithm is proposed based on some dispatching rules, ant colony optimization, and genetic algorithm. The performance of the proposed algorithm on some test instances is compared to the mixed integer linear programming model as well as the state-of-the-art algorithms called genetic algorithm, tabu search, bio-geography based optimization, and artificial bee colony. The computational experiments show that our proposed approach outperforms other algorithms and the results indicate efficiency and capability of the proposed algorithm in comparison with the traditional algorithms.

Nowadays, in majority of academic contexts, it has been tried to consider the highest possible level of similarities to the real world. Hence, most of the problems have complicated structures. Traditional methods for solving almost all of the mathematical and optimization problems are inefficient. As a result, meta-heuristic algorithms have been employed increasingly during recent years. In this study, a new algorithm, namely Seeker evolutionary algorithm (SEA), is introduced for solving continuous mathematical problems, which is based on a group seeking logic. In this logic, the seeking region and the seekers located inside are divided into several sections and they seek in that special area. In order to assess the performance of this algorithm, from the available samples in papers, the most visited algorithms have been employed. The obtained results show the advantage of the proposed SEA in comparison to these algorithms. At the end, a mathematical problem is designed, which is unlike the structure of meta-heuristic algorithms. All the prominent algorithms are applied to solve this problem, and none of them is able to solve.