## 1 Introduction
Similar to IEAs, metaheuristic-based methods have been very quickly developing for solving the ELD problem, and there have been a large number of methods developed from the metaheuristic algorithms such as Particle swarm optimization (PSO) [18], Quantum-inspired particle swarm optimization (QIPSO) [19], Particle Swarm Optimization and Tabu Search Algorithm (DSPSO-TSA) [20], Fuzzy and Self-Adaptive Particle Swarm Optimization (FSAPSO) [21], Theta Particle Swarm Optimization (9-PSO) [22], Modified Particle Swarm Optimization (MPSO) [23], Artificial Immune System Algorithm (AISA) [24], Biogeography-based Optimization (BBO) [25], Chaotic Firefly Optimization Algorithm (CFOA) [26], Cuckoo Search Algorithm (CSA) [27], Modified Cuckoo Search Algorithm (MCSA) [28], One Rank Cuckoo Search Algorithm (RCSA) [29], CSA [30], MCSA [31], Krill Herd Algorithm (KHA) [32;33], Modified Krill Herd Algorithm (MKHA) [34], Modified Firefly Algorithm (MFA) [35], Varying Firefly Optimization Algorithm (VFOA) [36], Firefly Optimization Algorithm (FOA) [37], Improved Firefly Optimization Algorithm (IFOA) [38], Effectively Modified Firefly Algorithm (EMFA) [38], Immune Algorithm (IA) [39], Grey Wolf Optimization Algorithm (GWOA) [40], Chaotic Bat Optimization Algorithm (CBOA) [41], Exchange Market Algorithm (EMA) [42], Antilon Optimization Algorithm (ALO) [43;44], and Spotted Hyena Optimizer (SHO) [45]. Among the metaheuristic-based methods, EMFA [38] has been tested on many systems with complicated constraints such as transmission losses, POZs, ramp rate limits, spinning reserve, and fuel consuming characteristics such as MFs and VPEs. In addition, the method has been tried for very large-scale problems with a high number of generators and a high number of control variables. The results showed that the EMFA method provided the highest quality results compared to all other methods including the FOA, PSO, DE, CSA, and genetic algorithm (GA). In summary, improved methods have a good ability to search for better optimal solutions. Conversely, these methods become complex because of the combination of the original algorithms-based developed methods and modified mechanisms. In this paper, an improved antition optimization algorithm (IALO) is suggested for solving the ELD problem. Constraints such as transmission losses, POZ, RRL, and SR are considered. Moreover, we take into account the consuming characteristics of MFs and VPEs. In 2015, [PERSON] [43] suggested the antition optimization (ALO) algorithm for solving technology problems. The ALO method has been applied for solving the ELD problem in very small-scale electricity systems considering VPEs [44]. More research is needed to further clarify the efficiency of the ALO method. The suggested IALO method was tested on five different systems with different consuming characteristics and constraints. The results obtained by the IALO were analyzed, evaluated, and compared in other papers in the literature. The goals of the current work can be summarized as follows:  
1. Present all the computation steps of the ALO in detail and analyze drawbacks of the method;
2. Discuss improved versions of the ALOs that previous studies have proposed;
3. Propose a highly effective modification to produce a promising solution search strategy;
4. Combine our proposal with a modification that was proposed in a previous work. The combination of the highly effective proposed modification and the applied modification can support the proposed IALO method in finding optimal solutions effectively and quickly;
5. Consider the different systems that contain complex objective functions and constraints;
6. Compare our proposed IALO with classic ALO. Our proposed IALO can achieve much better results than those of the ALO with the same settings for control parameters. The proposed method is better or equal to other existing methods in comparing quality of solution, but it is more robust than these methods when comparing computational steps.