In this paper, the analysis of recent advances in genetic algorithms is discussed. The genetic algorithms of great interest in research community are selected for analysis. This review will help the new and demanding researchers to provide the wider vision of genetic algorithms. The well-known algorithms and their implementation are presented with their pros and cons. The genetic operators and their usages are discussed with the aim of facilitating new researchers. The different research domains involved in genetic algorithms are covered. The future research directions in the area of genetic operators, fitness function and hybrid algorithms are discussed. This structured review will be helpful for research and graduate teaching.

/pdf/a-review-on-genetic-algorithm-past-present-and-future-2o5y8ru178.pdf

A review on genetic algorithm: past, present, and future

The neural network model of Job shop scheduling problem is built. The characteristics and properties of its solutions are studied. A Hybrid Genetic Algorithm, combining genetic algorithm with neural network, for Job shop scheduling problem is described. The corresponding simulation shows that our method is good for complex production scheduling, at calculation time and goodness.

A hybrid genetic algorithm for job shop scheduling problem

A multi-objective multi-population ant colony optimization for economic emission dispatch considering power system security

Economic load dispatch (ELD) is the main optimization task in power system operation. Minimizing the fuel cost by optimally setting the real power outputs from generators is the objective of ELD problem. In this work, ELD problem is addressed by considering three different cost functions. Real power generations are adjusted for minimizing the fuel cost by using flower pollination algorithm (FOA). This algorithm works on the basis of pollinating behavior of flowering plants. Unlike the other nature inspired algorithms, it follows only the levy flight mechanism for generating the population for the next generation. Being free from large number of parameters, the algorithm works well and there is no much difficulty in tuning to suit for different problems. The algorithm can be coded easily in any programming language. The proposed algorithm is tested on the standard IEEE-30 bus system and the results are compared with those of the other algorithms reported in the literature. The results are found to be improved and encouraging.

/pdf/flower-pollination-algorithm-applied-for-different-economic-33awq3agma.pdf

Flower Pollination Algorithm Applied for Different Economic Load Dispatch Problems

Bilevel Optimization: Theory, Algorithms, Applications and a Bibliography

In this paper, multi-sine cosine algorithm (MSCA) is presented to solve nonlinear bilevel programming problems (NBLPPs); where three different populations (completely separate from one another) of sine cosine algorithm (SCA) are used. The first population is used to solve the upper level problem, while the second one is used to solve the lower level problem. In addition, the Kuhn–Tucker conditions are used to transform the bilevel programming problem to constrained optimization problem. This constrained optimization problem is solved by the third population of SCA and if the objective function value equal to zero, the obtained solution from solving the upper and lower levels is feasible. The heuristic algorithm didn’t used only to get the feasible solution because this requires a lot of time and efforts, so we used Kuhn–Tucker conditions to get the feasible solution quickly. Finally, the computational experiments using 14 benchmark problems, taken from the literature demonstrate the effectiveness of the proposed algorithm to solve NBLPPs.

/pdf/multi-sine-cosine-algorithm-for-solving-nonlinear-bilevel-1hyns079nh.pdf

Multi-Sine Cosine Algorithm for Solving Nonlinear Bilevel Programming Problems

Multi-objective economic emission load dispatch problem with trust-region strategy

A penalty method with trust-region mechanism for nonlinear bilevel optimization problem

An active-set trust-region algorithm for solving warehouse location problem

Bi-level programming problem (BLPP) is an optimization problem consists of two interconnected hierarchical optimization problems. Solving BLPP is one of the hardest tasks facing the optimization community. This paper proposes a modified genetic algorithm and a chaotic search to solve BLPP. Firstly, the proposed algorithm solves the upper-level problem using a modified genetic algorithm. The genetic algorithm has modified with a new selection technique. The new selection technique helps the upper-level decision-maker to take an appropriate decision in anticipation of a lower level’s reaction. It distinguishes the proposed algorithm with a very small number of solving the lower-level problem, enhances the algorithm performance and fasts convergence to the solution. Secondly, a local search based on chaos theory has applied around the modified genetic algorithm solution. Chaotic local search enables the algorithm to escape from local solutions and increase convergence to the global solution. The proposed algorithm has evaluated on forty different test problems to show the proposed algorithm effectiveness. The results have analyzed to illustrate the new selection technique effect and the chaotic search effect on the algorithm performance. A comparison between the proposed algorithm results and other state-of-the-art algorithms results has introduced to show the proposed algorithm superiority.

https://www.mdpi.com/2073-8994/12/5/767/pdf

Yousria Abo-Elnaga

Papers

Multi-Sine Cosine Algorithm for Solving Nonlinear Bilevel Programming Problems

Multi-objective economic emission load dispatch problem with trust-region strategy

A penalty method with trust-region mechanism for nonlinear bilevel optimization problem

An active-set trust-region algorithm for solving warehouse location problem

Modified Evolutionary Algorithm and Chaotic Search for Bilevel Programming Problems