Over the last few decades, a number of differential evolution (DE) algorithms have been proposed with excellent performance on mathematical benchmarks. However, like any other optimization algorithm, success DE is highly dependent search operators and control parameters that are often decided priori. The selection parameter values itself combinatorial problem. Although considerable investigations conducted regards to selection, it known be tedious task. In this paper, algorithm uses new...

In recent years, several multi-method and multi-operator-based algorithms have been proposed for solving optimization problems. Generally, their performance is better than other that based on a single operator and/or algorithm. However, they do not perform consistently well over all the problems tested in literature. this paper, we propose an improved algorithm uses benefits of multiple differential evolution operators, with more emphasis placed best-performing operator. The by 10 5, 10, 15...

The dynamic economic dispatch problem is a high-dimensional complex constrained optimization that determines the optimal generation from number of generating units by minimizing fuel cost. Over last few decades, solution approaches, including evolutionary algorithms, have been developed to solve this problem. However, performance algorithms highly dependent on factors, such as control parameters, diversity population, and constraint-handling procedure used. In paper, self-adaptive...

Many real-world optimization problems are difficult to solve as they do not possess the nice mathematical properties required by exact algorithms. Evolutionary algorithms proven be appropriate for such problems. In this paper, we propose an improved differential evolution algorithm that uses a mix of different mutation operators. addition, is empowered covariance adaptation matrix strategy local search. To judge performance algorithm, have solved well-known benchmark well variety The...

Over the last two decades, many Genetic Algorithms have been introduced for solving optimization problems. Due to variability of characteristics in different problems, none these algorithms performs consistently over a range In this paper, we introduce GA with new multi-parent crossover variety The proposed algorithm also uses both randomized operator as mutation and maintains an archive good solutions. has applied solve set real world problems IEEE-CEC2011 evolutionary competition.

Over the last two decades, many Differential Evolution (DE) strategies have been introduced for solving Optimization Problems. Due to variability of characteristics in optimization problems, no single DE algorithm performs consistently over a range problems. In this paper, better coverage problem characteristics, we introduce framework that uses multiple search operators each generation. The appropriate mix operators, any given problem, is determined adaptively. proposed has applied solve...

This paper puts forward a proposal for combining multi-operator evolutionary algorithms (EAs), in which three EAs, each with multiple search operators, are used. During the evolution process, algorithm gradually emphasizes on best performing EA, as well operator. The proposed is tested CEC2014 single objective real-parameter competition. results show that has ability to reach good solutions.

The Resource-Constrained Project-Scheduling Problem (RCPSP) is an NP-hard problem which can be found in many research domains. optimal solution of the RCPSP problems requires a balance between exploration/exploitation and diversification/intensification. With this mind, quantum-inspired evolutionary algorithms' ability to improve population quality solutions, work investigates performance genetic algorithm (QIGA), has been adapted with RCPSPs. proposed QIGA possesses same structure as...

Evolutionary algorithms have shown their promise in coping with many-objective optimization problems. However, the strategies of balancing convergence and diversity effectiveness handling problems irregular Pareto fronts (PFs) are still far from perfect. To address these issues, this paper proposes an adaptive sorting-based evolutionary algorithm based on idea decomposition. First, we propose environmental selection strategy. Solutions each subpopulation (partitioned by reference vectors)...

Over the past few years, success of multi-operator and multi-method algorithms encouraged researchers to combine them within a single framework. Although these have shown promising results, there are still rooms for further improvements. In this paper, we propose new way combining multiple evolutionary algorithms, each which may run with search operators. its process, algorithm gradually places emphasis on better-performing algorithm, as well own Such process is designed based quality...

Determining the Nash equilibria (NEs) in a competitive electricity market is challenging economic game problem. Although finding one equilibrium has been well studied, detecting multiple ones more practical and difficult, with few attempts to solve such discrete problems. However, most of reallife problems, an energy continuous containing infinite sets strategy that can be adopted by each player. Therefore, this paper, co-evolutionary approach proposed for NEs single run involving games...

The popularity of generative text AI tools in answering questions has led to concerns regarding their potential negative impact on students' academic performance and the challenges that educators face evaluating student learning. To address these concerns, this paper introduces an evolutionary approach aims identify best set Bloom's taxonomy keywords generate have low confidence answering. effectiveness is evaluated through a case study uses from Data Structures Representation course being...

Over the last two decades, many Genetic Algorithms have been introduced for solving Constrained Optimization Problems (COPs). Due to variability of characteristics in different COPs, none these algorithms performs consistently over a range problems. In this paper, we introduce Algorithm with new multi-parent crossover variety COPs. The proposed algorithm also uses randomized operator instead mutation and maintains an archive good solutions. has tested by 36 test instances, CEC2010...

Recently, the success history based parameter adaptation for differential evolution algorithm with linear population size reduction has been claimed to be a great solving optimization problems. Neuro-dynamic is another recent approach that shown remarkable convergence certain problems, even high dimensional cases. In this paper, we proposed new by embedding concept of neuro-dynamic into modified reduction. We have also an adaptive mechanism appropriate use and during search process. The...