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<p>Genetic algorithm is a well-known metaheuristic method to solve optimization problem mimic the natural process of cell reproduction. Having great advantages on solving optimization problem makes this method popular among researchers to improve the performance of simple Genetic Algorithm and apply it in many areas. However, Genetic Algorithm has its own weakness of less diversity which cause premature convergence where the potential answer trapped in its local optimum.  This paper proposed a method Multiple Mitosis Genetic Algorithm to improve the performance of simple Genetic Algorithm to promote high diversity of high-quality individuals by having 3 different steps which are set multiplying factor before the crossover process, conduct multiple mitosis crossover and introduce mini loop in each generation. Results shows that the percentage of great quality individuals improve until 90 percent of total population to find the global optimum.</p>

Loss issue is significant in power system since it affects the operation of power system, which ultimately can b e translated to monetary effect. Incremental IntroductionElectricity demand is reported to be increasingin many parts of the world. To ensure smooth and continuous supply, more energy need to be produced. Adding new conventional fuel power plant would be a direct method, but the high economic cost and the gas emission effect that comes together deter the installation without proper planning.Renewable energy (RE) offers a sustainable green energy alternative to the carbon-emission fossil fuel. Many countries have decided to utilise large-scale RE, such as solar power, wind power and hydro power. An extensive review and discussion on integrating large-scale photovoltaic (LSPV) power generation in China are reported in [1]. A high penetration PV power plant connected to the distribution network feeder wasstudied by [2]. Technical challenges and solutions to overcome power system stability challenges due to LSPV integration worldwide were presented by [3]. Although some researchers foresee that currently available RE resource is sufficeto serve current demand, extensive planning to optimise the size and location of RE with constraints is needed. Reference [4] presents how they determine the lowest-cost mix of RE resources, demand response and energy storage to replace conventional fuels in Ontario, Canada. Without optimisation, the location and size of RE may cause more loss and cost.Many optimisation techniques have been employed and improvised in finding the best solution. Particle swarm optimization (PSO) was used in [5][6][7][8][9][10][11] to determine the best solution of their objective function with constraints. Subsequently, ant -colony optimisation (ACO) and symbiotic organism search (SOS) are other optimisation technique used in [12][13][14][15][16][17][18][19][20]. These swarm intelligence (SI) are mostly developed to address stationary optimisation problems, thus not the best method for dynamic problems [15].

<p>Nowadays, the location and sizing of distributed generation (DG) units in power system network are crucial to be at optimal as it will affect the power system operation in terms of stability and security. In this paper, a new technique termed as Immune Log-Normal Evolutionary Programming (ILNEP) is applied to find the optimal location and size of distributed generation units in power system network. Voltage stability is considered in solving this problem. The proposed technique has been tested on the IEEE 26 bus Reliability Test System to find the optimal location and size of distributed generation in transmission network. In order to study the performance of ILNEP technique in solving DG Installation problem, the results produced by ILNEP were compared with other meta-heuristic techniques like evolutionary programming (EP) and artificial immune system (AIS). It is found that the proposed technique gives better solution in term of lower total system loss compared to the other two techniques.<em></em></p>

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