The concurrent pursuit of renewable energy (RE) transition and circular economy (CE) objectives, introduces complex multi objective trade-offs among economic cost, environmental impact, social benefit, and energy security. Existing frameworks seldom integrate causal decision-making with multi-objective optimization under a unified structure. This paper proposes a novel integrated framework combining Decision-Making Trial and Evaluation Laboratory (DEMATEL), Analytic Network Process (ANP), a Chaotic Multi-Objective Honey Badger Algorithm (C-IMOHBA), and the VIKOR ranking method. The principal algorithmic contribution is the replacement of pseudo-random number generation in IMOHBA with a Tent-Logistic hybrid chaotic map. This deterministic integration effectively avoids premature convergence and eliminates degenerate zero security solutions that comprise 7% of the vanilla IMOHBA archive. Criteria weights derived from a five-expert DEMATEL-ANP elicitation prioritize environmental impact (32.13%) and economic cost (28.07%). These weights are applied in VIKOR to rank 300 Pareto-optimal solutions generated under IRENA-validated constraints. Numerical experiments confirm that C-IMOHBA achieves superior diversity and convergence metrics in 89.6 seconds, outperforming the 94.4 seconds vanilla baseline. The top-ranked compromise solution demonstrates a balanced operational profile: an LCOE of $19.08/MWh, emissions of 15.27 kg CO₂eq/MWh, employment of 1.827 jobs/MW, and a robust energy security index of 34.53. Ultimately, the framework provides a structured, reproducible decision-support methodology for policymakers addressing sustainable RE-CE system design.