Chaotic Election Algorithm

Abstract

A novel Chaotic Election Algorithm (CEA) is presented for numerical function optimization. CEA is a powerful enhancement of election algorithm. The election algorithm is a socio-politically inspired strategy that mimics the behavior of candidates and voters in presidential election process. In election algorithm, individuals are organized as electoral parties. Advertising campaign forms the basis of the algorithm in which individuals interact or compete with one other using three operators: positive advertisement, negative advertisement and coalition. Advertising campaign hopefully causes the individuals converge to the global optimum point in solution space. However, election algorithm suffers from a fundamental challenge: it gets stuck at local optima due to the inability of advertising campaign in searching solution space. CEA enhances the election algorithm through modifying party formation step, introducing chaotic positive advertisement and migration operator. By chaotic positive advertisement, CEA exploits the entire solution space, what increases the probability of obtaining global optimum point. By migration, CEA increases the diversity of the population and prevents early convergence of the individuals. The proposed CEA algorithm is tested on 28 well-known standard boundary-constrained test functions, and the results are verified by a comparative study with several well-known meta-heuristics. The results demonstrate that CEA is able to provide significant improvement over canonical election algorithm and other comparable algorithms.

Optimal representation to High Order Random Boolean kSatisability via Election Algorithm as Heuristic Search Approach in Hopeld Neural Networks

“…The coalition's policy has the potential to have a huge effect in attracting the largest number of people who support the party's manifesto in seeking global solutions. Within a fair period, this process would provide a collabora-tive candidate of equal fitness [12], [10]. Because of these features in the EA, the hybrid proposed model can reduce the number of iterations an HNN is needed during the learning process by ensuring that there is a minimal amount of error accrued after the experimentation.…”

“…The local search function have been partitioned into search spaces. The optimization procedure is inspired based on the voting process in human society [11], [10]]. Generally, the population of an individual is divided into two parties which later carry out a series of operations such as initialization, eligibility stages, advertisement and alliance.…”

“…These stratgiies are resulted in a stronger searching technique. The primary goal of EA is to encourage candidates to coverge on a global minimum solution (best solution) to optimzation problem [10]. Optimization process based on standard Hopfield model has a high probability of becoming caught at a suboptimal solution with the number of neurons firing into the network [3], [9], [7].…”

Stock exchange trading optimization algorithm: a human-inspired method for global optimization

“…The extended algorithms improve the basic operators or overcome the defections that exist in the conventional versions. For example, the chaotic election algorithm (CEA) [12] embeds the chaos-based advertisement operator to the conventional PEA algorithm [11] to improve its search capability and convergence speed. Some other algorithms that recently proposed and used in different applications are opposition-based learning firefly algorithm combined with dragonfly algorithm (OFADA) [69], random memory and elite memory equipped artificial bee colony (ABCWOA) algorithm [70], efficient binary symbiotic organisms search (EBSOS) [71,72], efficient binary chaotic symbiotic organisms search (EBCSOS) [73], and binary farmland fertility algorithm (BFFA) [74].…”

“…Human-inspired algorithms simulate the approaches that humans use to solve problems. The presidential election algorithm (PEA) [11,12] is a fundamental human behavior-inspired algorithm that models the interaction between voters and candidates in the election campaign. A few well-known human-inspired algorithms are football game algorithm (FGA) [13] inspired by the behavior of players to score a goal under the supervision of the coach; political optimizer (PO) [14] inspired by the multi-phased process of politics; heap-based optimizer (HBO) [15] inspired by the rank hierarchy in organizations, deer hunting optimization algorithm (DHOA) [1] simulates the hunting methods of the human toward deer; and nomadic people optimizer (NPO) [16] models the migration behavior of nomadic people in their searches and movement for sources of life including grass for grazing and water.…”